Available online at www.sciencedirect.com

ScienceDirect
DNA barcodes evolve for high-resolution cell lineage
tracing
Nanami Masuyama1,2,3,6, Hideto Mori1,2,3,6 andNozomu Yachie1,2,3,4,5
Mammalian development involves continuous dynamic
processes in which cells propagate, differentiate, orchestrate,and decease to produce high-order functions. Althoughaccurate cell lineage information can provide a strong
foundation to understand such complex processes, the celllineages involved in development of the whole mammalian
body remain largely unclear, except for in early embryogenesis,
which is observable under a microscope. With CRISPR
genome editing, the concept of ‘evolving DNA barcodes’ has
rapidly emerged for large-scale, high-resolution cell lineage
tracing, where cell-embedded DNA barcodes continuously
accumulate random mutations that are inherited from mother todaughter cells. Similar to evolutionary tree reconstruction using
species’ DNA sequences, cell lineages can be reconstructedusing shared mutation patterns in the DNA barcodes identified
using massively parallel sequencing. The dramaticdevelopments of single-cell and imaging technologies haveenabled analyses of the molecular and spatial architecture ofheterogeneous cells. The evolving DNA barcodes can also
consolidate this information on a reconstructed cell lineage tree
and accelerate our understanding of multicellular organisms.

Introduction

Addresses
1
Research Center for Advanced Science and Technology, The
University of Tokyo, Tokyo 153-8904, Japan
2
Institute for Advanced Biosciences, Keio University, Tsuruoka
997-0035, Japan
3
Graduate School of Media and Governance, Keio University, Fujisawa
252-0882, Japan
4
Department of Biological Sciences, School of Science, The Universityof Tokyo, Tokyo 113-0033, Japan
5
PRESTO, Japan Science and Technology Agency (JST), Tokyo
153-8904, Japan

Lineage tracing by live imaging enables the analysis ofdevelopmental dynamics of only systems that can beobserved under a microscope. Therefore, reverse (orretrospective) approaches have become the major meansof studying cell lineages of more complex systems, inwhich heritable markers are introduced into cells at an
early stage and their distributions at a later stage are
analyzed after system progression. For example, Brainbow enabled the tracing of cell clones in the course ofdevelopment of higher organisms, such as mice [4] andDrosophila [5], by labeling cells in an initial embryonic
population with the expression of a random combinationof fluorescent proteins induced via stochastic DNA
recombination. This fluorescent labeling method enables
the ‘painting’ of cells with different colors and analysis of
how they are distributed at a later stage with histologicalinformation obtained by fluorescent microscope observations. However, definitive conclusions about the clonalrelationship of two cells having the same fluorescentexpression pattern cannot always be drawn when thenumber of different fluorescent patterns exceeds or isclose to the number of initially labeled cells (Figure 1aand b).

Corresponding author:
Yachie, Nozomu (yachie@synbiol.rcast.u-tokyo.ac.jp)
6
These authors contributed equally to this work.
Current Opinion in Chemical Biology 2019, 52:63–71
This review comes from a themed issue on Synthetic biology
Edited by Hirohide Saito and Yohei Yokobayashi

https://doi.org/10.1016/j.cbpa.2019.05.014
1367-5931/ã 2019 Elsevier Ltd. All rights reserved.

The development of animals and plants starts from asingle fertilized cell and involves the formation of different organs and the whole body through cell division. In
this process, cells with distinct molecular phenotypes andfunctions dynamically emerge, interact, and cooperate toorganize functional units that also crosstalk at higher
levels. Complete information on the whole cell lineageof a given organism would provide a strong basis forsurveying its development. In the early 1980s, mappingof the entire cell lineage of the nematode Caenorhabditis
elegans from the fertilized egg through to the hermaphroditic adult, for exactly 959 cells, was completed based on
microscopic observations [1–3]. While this has long provided the foundation for study of the developmental
biology of C. elegans, the developmental cell lineages ofmammalian and other organisms have remained largely
elusive.

Tracing of clonal cell distributions using static
markers

The concept of a complex DNA barcode library has been
adopted to expand the resolution of this approach(Figure 1c) [6,7,8–11]. For example, by lentivirus
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Current Opinion in Chemical Biology 2019, 52:63–71

64 Synthetic biology

Figure 1(a)(b)

Sparse fluorescent labeling

Dense fluorescent labeling(c)

DNA barcode labeling

Clone labeling

Clone labeling

Clone labeling

Spatial observation

Spatial observation

Sequencing-based observationof different cells or groups

(d)

(e)
Chromosomes

Flow of cell division

Spontaneous mutations

Evolving DNA barcode

Flow of lineage reconstruction based on mutations
Current Opinion in Chemical Biology

Various cell labeling methods for developmental lineage analyses. (a, b) Sparse fluorescent labeling (a) and dense fluorescent labeling (b). Cells in
an initial population are labeled with a limited set of fluorescent markers. After cell progression, the spatial distribution of the labeled clones canbe observed by imaging analysis. (a) Sparse fluorescent labeling traces distinct clones with low clone coverage. (b) Dense fluorescent labelingtraces clones with high coverage but with ambiguous distinction of different clones. (c) DNA barcode-based labeling. This method enables highcoverage and distinct labeling of clones but the spatial information is lost in the pyrosequencing-based readout. (d) Lineage tree reconstruction
using spontaneous somatic mutations. (e) Lineage tree reconstruction using an evolving DNA barcode.

transduction of random DNA barcodes, different clones
in a cancer cell population can be uniquely labeled withdifferent DNA barcodes [6]. Massively parallel sequencing of DNA barcodes obtained from time-course samplesof the population enables en masse quantification of clone
size dynamics in various conditions, such as in the presence of anticancer compounds. Other studies have also
used a complex DNA barcode library for hematopoietic
lineage tracing, where hematopoietic stem cells (HSCs)
are harvested from mice, uniquely barcoded using lentivirus, and transplanted back into another mouse(Figure 2a) [7,8]. After the in vivo differentiation of
the barcoded HSCs, blood cells are harvested from thebone marrow and fractionated into different cell types
according to their immunophenotypes using flow cytometry cell sorting. DNA barcodes of each fractionatedpopulation are then analyzed by massively parallel
Current Opinion in Chemical Biology 2019, 52:63–71

sequencing, and finally the genealogical relationshipsof different cell type groups are reconstructed according
to the similarity between the DNA barcode distribution
profiles.
However, this approach can potentially interfere with thenative hematopoietic process, via lentiviral infectionand HSC transplantation. Several in vivo cell barcoding
methods have been developed to avoid this problem
in hematopoietic lineage analysis [12,13]. Using a concept similar to that applied in Brainbow, a recent study
established a mouse line harboring a Polylox barcode, achromosome-encoded array of DNA barcodes each sandwiched by lox sites orientated in the opposite directions(Figure 2b) [12]. Inducing the expression of site-specific
Cre recombinase in an early embryo of its offspring, cells
including HSC progenitors were uniquely labeled bywww.sciencedirect.com

Evolving DNA barcodes Masuyama, Mori and Yachie 65

Figure 2

(a)

(b)
CLP

CD4

B

Gr
BC1

BC2

BC3

BC4

BC5

BC6

BC7

BC8

BC8

BC9

BC9

BC9

Deletion
BC1

Barcode profiles by sequencing

BC2

BC5

BC7

Inversion

Lineage prediction bybarcode profile similarity

Transplantation

BC6

BC1

BC2

BC5

BC8

Lentivirustransduction

BC7

HSC

Cell sorting

HSC

BC6

DNA barcode library

CD4

Harvestblood cells

MPP/
Flk2-

MPP/
Flk2+

CLP

Deletion

CD8
B

GMP

BC1

BC6

HSC

BC9

Gr
MEP

loxP sites

Recombining loxP sites
Current Opinion in Chemical Biology

Tracing hematopoiesis by a complex library of static DNA barcodes. (a) Lentiviral barcoding of cells. HSCs are harvested from mice and uniquelylabeled by a highly complex lentiviral library of DNA barcodes. The barcoded cells are transplanted into a mouse. After differentiation, cells are
harvested from the bone marrow and sorted into different cell types. The differentiation lineage can be inferred according to the similarities
between the DNA barcode profiles of the fractionated cell groups. (b) An example of an in vivo Polylox barcoding pathway to randomly assign aunique barcode to a clone.

random drop-offs and inversions in the DNA barcode
cassettes. Similar to the lentiviral barcoding approach, thepattern profiles of Polylox barcodes in different groups ofdifferentiated cells successfully reconstructed the mosaic
structure of HSC developmental patterns, where somemajor HSC clones gave rise to multilineage fates, such as
the canonical tree-like view of HSC differentiation, butsome other major clones committed to oligolineage fates.

Cell lineage tree reconstruction using somatic
mutations
While the above-mentioned approaches capture how
different markers that are introduced into cells of an
initial population are distributed and expanded at thetime of observation, these methods do not directly capture the structures of branching cell lineage trees. Chromosomes of dividing cells are replicated and segregatedfrom mother cells to daughter cells with spontaneous
mutations, each of which can be treated as a new labelof cells discriminating their downstream branches of cell
division from the others. Therefore, similar to phylogenetic tree reconstruction using the genomic sequences ofdiverged species, the reconstruction of cell lineages ofdaughter cells using their somatic mutation patterns was
proposed (Figure 1d). Examples of this include
inferences about abstract cell lineages using indels andsingle-nucleotide polymorphisms (SNPs) [14,15], mobile
retrotransposon elements [16], microsatellites [17–20],and copy number variations (CNVs) [21,22].
In such studies, the resolutions of the reconstructed celllineages are highly limited, mainly due to the fact thatwww.sciencedirect.com

spontaneous mutations of individual cells rarely occur
across the genome. Some studies have used single-cellgenome sequencing to reconstruct cell lineages directlyfrom daughter cells [15,17,18,22]. However, the sensitivity and coverage of single-cell genome sequencing
technologies have been limited to thoroughly surveying
de novo mutations and differences in chromosomal
structures. Sequencing errors by these approaches
could also lead to erroneous lineage reconstruction.
Other studies have employed whole-genome sequencing of local tissue samples or distinct cell type groups ofdifferent immunophenotypes to reconstruct theirlineages [16,19–21], assuming that such cell type groups
are derived from similar cell lineage clades. Thisapproach allows robust reconstruction of cell lineages
from high-coverage genomic sequences for the upper
lineage hierarchy above the population averages of thedefined cell groups, but an invalid assumption about
the lineage-restricted cell type groups might distort theresults. Notably, another type of study successfullyreconstructed an accurate cell lineage of mouse somatic
cells using organoid technology, where organoids
were established from the stomach, intestines, andprostate of mice to obtain abundant cells for highcoverage genome sequencing of their parental single
cells [14]; however, in terms of its practical application,
this method was limited to specific types of cell fromwhich organoids can be established. In any of the
genome sequencing-based lineage tracing approaches,
the scalability in number of single-cell or local cell typegroup samples has been highly limited by the cost andthroughput of current sequencing methods.
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66 Synthetic biology

Evolving DNA barcodes

The DNA replication error rate of 108 and the DNA
error correction efficiency of 99% suggest that one newsomatic mutation occurs in a mammalian genome ofbillions of base pairs in approximately every dozen cell
divisions. Thus, a somatic ‘mutatome’ in the trillions of
single cells comprising the mammalian body could nearlycompletely reconstruct the whole cell lineage of the bodydevelopment. However, this concept is ahead of its time,as there is currently no technology that can efficiently andeconomically identify combinatorial mutation information spread across the genome of every cell. In contrast,
CRISPR–Cas9 has emerged as a versatile genome editing
tool that can induce mutations in target DNA regions ofvarious organisms through sequence complementarity ofguide RNA (gRNA); since its establishment, there havebeen rapid, turbulent implementations of the concept of‘evolving DNA barcodes’ for lineage tracing (Figure 1e)[23,24,25,26,27,28–30,31]. In this concept, a cellembedded synthetic DNA barcode(s) continuously
acquires new mutations and replicates along with cell
divisions. Unlike somatic mutations in the genome, all of
the lineage branching markers in the end-point daughter
cells can be efficiently identified by sequencing of thelocal DNA barcode regions.
In 2016, GESTALT (genome editing of synthetic target
array for lineage tracing) was the first reported approach
among many developed for cell lineage tracing using the
evolving DNA barcode [23]. In this study, a transgenic
zebrafish harboring an array of ten different DNA barcodes was first established. For lineage tracing, a one-cellembryo of this barcoded zebrafish was obtained andinjected with Cas9 ribonucleoproteins (RNPs) containing
gRNAs targeting the ten corresponding barcodes. Afterdevelopment, different organs were dissected, fromwhich cells were fractionated into different cell typegroups; their DNA barcodes were recovered by PCR
amplification and identified by massively parallel
sequencing. In the clades and subclades of the celllineage reconstructed from the unified barcode sequencing results, there were increasing levels of lineage-specificrestriction of specific cell type groups from the upperhierarchical levels of the lineage to downstream, indicating the validity of this approach.
Although GESTALT requires three different components—Cas9, gRNAs, and a DNA barcode array—a morecompact system has been proposed. The original CRISPR–
Cas9 genome editing requires the protospacer adjacentmotif (PAM) sequence (50 -NGG-30 for SpCas9 commonlyused in these studies) next to the 30 end of the target
sequence, which discriminates the gRNA-encoding
DNA region and its target having the same sequence. In
contrast, the concept of engineered homing gRNA
(hgRNA) [24] or self-targeting gRNA (stgRNA) [25] hasbeen proposed, in which the mutant gRNA encodes a PAM
Current Opinion in Chemical Biology 2019, 52:63–71

sequence adjacent to the targeting sequence region anditeratively mutates its own coding sequence. This gRNA
mutant has also been used to trace cell lineages, but its
resolution is limited because the iterative editing quickly
terminates when its functional coding region including the
intrinsic PAM is disrupted by mutations after a few rounds.
Similar to gRNA, the editing speed of hgRNA/stgRNA
depends on its targeting sequence [24] and is controllable by adjusting the Cas9 dose [25]. Furthermore, thenumber of self-editing cycles can be increased to a certain
degree by adding an extra filler sequence upstream of thehgRNA/stgRNA-coding region, so that it can serve as a
new targeting sequence when the original targeting
sequence is deleted by the self-targeting [24,25,26].
Using combinatorial information of many different
hgRNAs having various editing speeds, a proof-of-concept of mammalian developmental lineage tracing wasrecently demonstrated [26]. In this study, a foundermouse MARC1 (mouse for actively recording cells) having 60 chromosome-encoded hgRNAs was derived from a
population of engineered embryonic stem (ES) cells eachhaving high copy number hgRNA transgenes introduced
by PiggyBac transposon technology. Upon crossing a
MARC1 mouse with a Cas9-expressing mouse, the development of their E12.5 conceptus was examined by analyzing mutation patterns of hgRNAs in different sectionsof the placenta and yolk sac, as well as the heart and a limb
bud of the embryo. The developmental lineage for different brain sections was also reconstructed by thisapproach, showing that the anterior–posterior axis isestablished before the commitment to the lateral axis.
Although this was the first demonstration of whole-bodylevel mammalian lineage tracing with the evolving DNA
barcodes, this method had a similar issue to the somatic
mutation-based lineage tracing, where mutations accumulated in hgRNAs of distant chromosomal loci in which
combinatorial information is difficult to obtain from single
end-point daughter cells.

Scaling up lineage tracing using single-cell
technologies
The evolving DNA barcodes only record information for
topological structures of lineage trees. Therefore, mapping of functional or histological annotations of end-point
daughter cells or population groups to the reconstructedlineage is necessary to address their biology. The rapid
progress in single-cell technologies has enabled highthroughput molecular profiling of cells and revealed thatheterogeneous biological systems are composed of manybut distinct groups of unknown cell types, presenting a
new challenge regarding how they emerge and orchestrate functions and structures within a system [32]. The
throughputs of single-cell transcriptome (single-cell RNA
sequencing or scRNA-seq) methods have also been dramatically enhanced by the molecular DNA barcode concept. For example, in the water-in-oil droplet-basedwww.sciencedirect.com

Evolving DNA barcodes Masuyama, Mori and Yachie 67

scRNA-seq technologies [33,34], single cells are encapsulated by droplets with a single unique bead particle
containing oligo(dT) reverse-transcription primersconcatenated to bead-specific DNA barcodes, which
eventually serve as ‘cell-specific’ barcodes. The transcriptome of each cell is reverse-transcribed and labeled by thecell-specific barcodes in a droplet. After breaking up thedroplets, the barcoded cDNAs are pooled for sequencing,
and the sequencing reads are demultiplexed according to
the cell-specific barcodes.
The evolving DNA barcode concept has been integrated
into this scRNA-seq approach. In this concept, the evolving
DNA barcodes are transcribed as polyadenylated RNA(evolving RNA barcodes), so their sequence or induced
mutation patterns can be identified with transcriptome
profiles at single-cell resolution. scGESTALT was one
such example that achieved simultaneous single-cell profiling of cell types and their cell lineages for zebrafish brain
development (Figure 3a) [27]. Using scGESTALT, morethan 100 distinct cell types with unique gene expression
patterns were identified along with their cell lineages from
60 000 single-cell transcriptomes of juvenile zebrafish
brain, shedding light on how gene expression cascades

commit different cell types to restricted lineages andhow different brain regions develop.
In the MARC1 mouse-based lineage tracing using manydifferent hgRNAs, lineage marker mutations arise indistant loci of the genome and their combinations cannot
be identified efficiently at the single-cell level. In
GESTALT (and in scGESTALT), random mutations
are all induced in a single array of targeting barcodes,
whereby the mutational combinations of a cell can all beidentified in a single sequencing read of massively parallel pyrosequencing. However, owing to the limit ofsequencing read length, the barcode array cannot beelongated so it can harbor a higher number of mutationalvariants, capable of recoding lineages of all of the trillions
of cells in the adult mammalian body. A computational
simulation study has also suggested that some of thecurrent lineage tracing methods have a limitation in
amendable information content in their evolving DNA
barcodes to uniquely label all of the cell lineage branches
in a developmental process [35]. These issues could
also be resolved using the concept of the evolving
RNA barcode with scRNA-seq. In LINNAEUS (lineage
tracing by nuclease-activated editing of ubiquitous

Figure 3(a)(c)

Transcribing GESTALT
barcodeAAAAAtSNE 2

scRNA-seq

Cell typing

Lineagereconstruction

Transcriptome
AAAAA
AAAAA
AAAAA

(b)

tSNE 1Three-dimensional
lineage reconstruction

Scratchpad RNA

Spatialobservation

PiggyBac
terminal repeat

Scratchpad Barcode(10x repeat)

Scratchpadspecific probes

gRNA
Cas9

smFISHCRISPR–Cas9induced collapse
BarcodeBarcodespecific probes

Progressive
scratchpad collapse
Current Opinion in Chemical Biology

Lineage tracing methods for simultaneous annotation of cells. (a) scGESTALT. An evolving DNA barcode is transcribed as polyadenylated RNA.
Mutation patterns in the evolving RNA barcodes of different cells can be identified with their corresponding single-cell transcriptome data,
providing simultaneous observation of cell type and lineage. (b) MEMOIR. The founder cell has many transgenes expressing different barcodes
concatenated to a common ‘scratchpad’ encoding a tandem repeat of gRNA targeting sites. As the cell divides, each scratchpad is collapsed by
CRISPR–Cas9. Scratchpad statuses of different barcodes are identified for each cell with spatial information using seqFISH. (c) Coupled withhigh-capacity tissue imaging and in situ RNA sequencing technologies, an evolving DNA barcode system would enable high-resolution lineage
reconstruction of cells from the whole mammalian body with their spatial information.
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Current Opinion in Chemical Biology 2019, 52:63–71

68 Synthetic biology

sequences) [28] and ScarTrace [29], developmental
lineages of zebrafish were reconstructed via the CRISPR–Cas9 editing of ubiquitous sequences in RFP or GFP
transgenes expressed from multiple independent genomic loci, in which the whole of the induced mutations ineach cell was read by scRNA-seq together with cell type
information. A better approach for lineage tracing inmouse development has been proposed more recently[30]; in this method, unlike in LINNAEUS or ScarTrace,
unique DNA identifiers are introduced into the different
transgene barcode units, all of which have a common arrayof three gRNA targeting sites, so mutation products of thesame barcode loci in different single cells can be related
directly. However, although the capacity for recording
cell lineage information in an evolving DNA barcode
system can be elevated accordingly using scRNA-seq, abottleneck inhibiting large-scale cell lineage reconstruction using this strategy has remained as an obstacle, due tolimited scalability of scRNA-seq, which is currently
capable of processing only up to dozens of thousandsof single cells.

Deciphering cell lineages with spatial
information
Immediately after the first CRISPR-based evolving DNA
barcode GESTALT, another proof-of-concept method,
MEMOIR (memory by engineered mutagenesis with
optical in situ readout), was reported for the simultaneous
identification of spatial information of cells and theirdividing lineages at a small scale [31]. In this study,
multiple different transcribing barcodes concatenated to acommon ‘scratchpad’, a tandem repeat of ten identicalgRNA targeting sequences, were introduced into parental
mouse ES cells (Figure 3b). The scratchpads of cells werethen irreversibly modified by Cas9 and gRNA along withcell divisions. By a multiplexed single-molecule RNA
fluorescence hybridization (smFISH) method, namely,
seqFISH [36,37], the transcribed barcode molecules weredetected using the probing intensity for remaining intact
repeats in the scratchpad, allowing reconstruction of thelineage of daughter cells based on the combinatorial
barcoded scratchpad statuses. Combined with the simultaneous smFISH imaging of the pluripotency regulator
Esrrb, this MEMOIR experiment showed that the shortterm state switching of ES cells is also lineage-restrictedfrom parental cells.
Although MEMOIR has been demonstrated only forsmall-scale lineage tracing from a single parental cell to
up to eight daughter cells, this concept provided a foundational idea for current technological developments
towards interpretation of the development and functional
expression of multicellular organisms with both theirunderlying lineage and spatial structures. The rapid
development of recent tissue clearing and imaging methods has enabled the exploration of spatial molecular
distributions at the whole-organ and whole-body levels
Current Opinion in Chemical Biology 2019, 52:63–71[38]. Whereas these methods have mainly focused on
tissue localization of a given set of target proteins taggedby immunofluorescent probes or fused to fluorescentmarker proteins, deep-tissue RNA imaging coupled with
the MEMOIR-like approach could achieve spatial tracingof the lineages of large organs and bodies. Furthermore,unlike FISH-based imaging, which requires prior information about target RNA sequences, extensive efforts
have been devoted to the de novo sequencing of RNAs in
three-dimensional intact tissues, as represented bySTARmap technology [39]. This prompted us to envision
the spatial reconstruction of a high-resolution cell lineage
tree by a parallel set of scGESTALT-like evolving RNA
barcodes that continuously accumulate random mutations
to label unique lineage branches in a high-content manner (Figure 3c).

Outlook and discussion
The process of cell division can be traced by live imaging
under a microscope only for a small system, similar to thedevelopment of C. elegans. Fluorescent labeling of cells
allows us to analyze the spatial distributions of labeledclones in a larger system with a certain resolution, which islimited by the number of fluorescent markers. DNA
barcode-based unique labeling of a large number of cells
coupled with pyrosequencing sacrifices spatial information of cells but enables high-capacity measurements ofclone size dynamics and the distribution of clones into
different types of cell, as seen in the examples of hematopoietic cell lineage analysis.
The concept of evolving DNA barcodes has appeared toenable the reconstruction of cell lineage trees of complex
systems using CRISPR–Cas9 genome editing. However,these methods require further improvement because
CRISPR-based evolving DNA barcodes have been demonstrated in several studies to saturate or stop in earlier
phases of the developmental process [23,26,27]. An
ideal system would instead continuously accumulate
unique new mutations in evolving barcodes at every cell
division to entirely label all of the branches of the target
cell lineage. Although the validity of the current lineage
tracing methods has often been shown only by specificcell types being restricted or localized in thereconstructed lineages, none of the previous studies
quantitatively assessed the accuracy of their lineage
reconstruction methods. We here propose that the performance of an evolving DNA barcode system could bequantified if the same target lineage can be reconstructedusing orthogonal sets of independent evolving DNA
barcodes (Figure 4). For example, if multiple evolving
DNA barcodes with distinct identifier sequences aretranscribed from different chromosomal loci, subgroupsof them can be used for separate lineage reconstructions
with scRNA-seq and the agreement between the reconstructed lineages can then be quantitatively compared,for example, using Robinson–Fould distance.
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Evolving DNA barcodes Masuyama, Mori and Yachie 69

Figure 4

Cell
Evolving DNA Locus-specificbarcodebarcode...

...

Barcode Set 1

Barcode Set 2

Lineage reconstructed byBarcode Set 2

Quantification of topology agreement(ex. by Robinson-Foulds distance)

Lineage reconstructed byBarcode Set 1

Branch ambiguity
Current Opinion in Chemical Biology

Quantitative measurement of the resolution and accuracy of a lineage tracing method. For a lineage tracing method that utilizes many evolving
DNA barcodes per cell, multiple cell lineage trees can be predicted from orthogonal sets of evolving DNA barcodes and the accuracy of themethod can be measured by their agreement. Since mother cells always divide into two daughter cells, the resolution of the reconstructed lineage
can also be formulated by combining the lineage agreement and distribution in numbers of branching edges of middle-layer nodes.

DNA barcodes serve not only as indices of cell clones andlineage branches, but also as addresses to link single-cell
profiles and spatial positions of molecules and cells whencoupled with scRNA-seq and imaging technologies,
respectively. Several studies have demonstrated simultaneous analyses of cell types and their lineages at certain
scales using scRNA-seq. MEMOIR has successfully
traced a small-scale lineage of eight mouse ES cells with
their spatial positions using seqFISH. With further
advancements in single-cell and imaging technologies,
an evolving DNA barcode system could accurately decipher whole-body cell lineages of large multicellularorganisms beyond C. elegans with high-content cell typeand spatial information. This approach, however, only
maps information of cells obtained at the time of observation to the marginal edges of a reconstructed celllineage, which is only the one that constitutes information
tracing back into the past, and cannot address how different cells dynamically change their statuses across thelineage. An interesting simulation study was reported in
the MEMOIR paper, in which the developmental histories of cellular events could also be reconstructed with celllineages by transmitting targeted event signals to scratchpad editing [31]. As discussed in another review on thisissue [40], the development of DNA event recording
systems has become another large field of synthetic
biology, where various environmental and intracellular
events are recorded in DNA tapes of cells using CRISPRrelated methods and DNA recombinations. While theircurrent implementations remain at the proof-of-concept
stage, many of these approaches should theoretically becompatible to lineage tracing using the evolving DNA
barcodes. Accordingly, the evolving DNA barcodesshould further accelerate our understanding of various
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biological systems, including the entire developmental
process of mammals.

Conflict of interest statement
Nothing declared.

Acknowledgements
We thank members of the Yachie laboratory for discussions about different
aspects of the current and past cell lineage tracing approaches. The research
projects of the Yachie laboratory in this field are supported by grants fromthe Japan Science and Technology Agency (JST) PRESTO and CREST
programs, the New Energy and Industrial Technology Development
Organization (NEDO) Smart Cell Project, the Japan Agency for Medical
Research and Development (AMED) PRIME program, the Shimadzu
Science Foundation, the Takeda Science Foundation, the SECOM Scienceand Technology Foundation, the Naito Foundation, the Nakajima
Foundation, and the Asahi Glass Foundation.

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