Synthetic recording and in situ readout of lineage information in single cells

Synthetic recording and

in situ

readout of lineage information in

single cells

Kirsten L. Frieda

1,*

James M. Linton

1,*

Sahand Hormoz

1,*

Joonhyuk Choi

Ke-Huan K.

Chow

Zakary S. Singer

Mark W. Budde

Michael B. Elowitz

1,3,§

, and

Long Cai

2,§

Division of Biology and Biological Engineering, California Institute of Technology, Pasadena,

California 91125, USA.

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena,

California 91125, USA.

Howard Hughes Medical Institute, California Institute of Technology, Pasadena, California 91125,

USA.

Abstract

Reconstructing the lineage relationships and dynamic event histories of individual cells within

their native spatial context is a long-standing challenge in biology. Many biological processes of

interest occur in optically opaque or physically inaccessible contexts, necessitating approaches

other than direct imaging. Here we describe a synthetic system that enables cells to record lineage

information and event histories in the genome in a format that can be subsequently read out of

single cells

in situ.

This system, termed memory by engineered mutagenesis with optical

in situ

readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The

state of a given scratchpad can be irreversibly altered by CRISPR/Cas9-based targeted

mutagenesis, and later read out in single cells through multiplexed single-molecule RNA

fluorescence hybridization (smFISH). Using MEMOIR as a proof of principle, we engineered

mouse embryonic stem cells to contain multiple scratchpads and other recording components. In

these cells, scratchpads were altered in a progressive and stochastic fashion as the cells

proliferated. Analysis of the final states of scratchpads in single cells

in situ

enabled reconstruction

of lineage information from cell colonies. Combining analysis of endogenous gene expression

with lineage reconstruction in the same cells further allowed inference of the dynamic rates at

which embryonic stem cells switch between two gene expression states. Finally, using simulations,

we show how parallel MEMOIR systems operating in the same cell could enable recording and

readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for

information recording and

in situ

, single-cell readout across diverse biological systems.

Reprints and permissions information is available at

www.nature.com/reprints

Correspondence and requests for materials should be addressed to L.C. (lcai@caltech.edu) or M.B.E. (melowitz@caltech.edu).

These authors contributed equally to this work.

These authors jointly supervised this work.

Author Contributions

K.L.F. and J.M.L. performed the experiments with assistance from S.H., J.C., K.K.C. and Z.S.S.; K.L.F. and

S.H. analysed the data; S.H. performed the simulations; M.B.E. and L.C. supervised the project. All authors wrote the manuscript.

The authors declare competing financial interests: details are available in the online version of the paper.

Supplementary Information

is available in the online version of the paper.

HHS Public Access

Author manuscript

Nature

. Author manuscript; available in PMC 2019 April 29.

Published in final edited form as:

Nature

. 2017 January 05; 541(7635): 107–111. doi:10.1038/nature20777.

Author Manuscript

Somatic mutations occur stochastically and independently in different cells, and are

inherited from one cell generation to the next. They can therefore leave a record of lineage

relationships, or other information, in the genomes of related cells. Pioneering work showed

that sequencing can be used to identify somatic mutations and thereby recover lineage

information

–

. However, sequencing has generally required disrupting the spatial context of

cells, and somatic mutations are distributed throughout the genome, hindering their

identification and analysis. Two recent advances together enable an alternative approach.

First, CRISPR/Cas9 (refs

–

) can target mutagenesis to specific genomic elements,

facilitating the continuous and controlled generation of stochastic genetic variation at

designated genomic regions. Second,

in situ

single cell analysis by sequential smFISH

(seqFISH) allows genetic information to be directly interrogated in a highly multiplexed

fashion in individual cells within native tissue. Together, these techniques could in principle

permit recording and

in situ

readout of genetic changes at specific loci for lineage

reconstruction and event recording.

To implement such a system, we devised a bipartite genetic recording element termed the

‘barcoded scratchpad’. The state of this scratchpad can be stochastically altered in live cells

and read out

in situ

in single cells by smFISH (Fig. 1a, Extended Data Fig. 1a). The

scratchpad element consists of 10 repeat units

. gRNA targeting of Cas9 to the scratchpad

generates double-strand breaks that result in its deletion, or ‘collapse’. (Fig. 1a, b). Adjacent

to each scratchpad, we incorporated a co-transcribed barcode (Supplementary Table 1). The

barcode and scratchpad components can each be identified using specific sets of smFISH

probes (Supplementary Table 2), and thus serve as an addressable ‘bit’.

Using a pool of such barcoded scratchpads enables lineage recording and readout through a

two-step process. During cell proliferation, Cas9 generates gradual and stochastic

accumulation of collapsed scratchpads in each cell lineage. Subsequently, cells can be fixed

and analysed by seqFISH to identify barcodes and assess their states based on the presence

or absence of a co-localized scratchpad signal (Fig. 1c).

To implement the MEMOIR system, we engineered a stable mouse embryonic stem (ES)

cell line, designated MEM-01, incorporating barcoded scratchpads, Cas9, and a scratchpad-

targeting gRNA (Fig. 1b). First, we used PiggyBac transposition

to integrate a set of 28

barcoded scratchpad elements into the genome. We identified a clone in which 13 different

barcodes were highly expressed (Extended Data Fig. 1b–d). Within this line, we stably

integrated a Cas9 variant containing an inducible degron to allow external modulation of

Cas9 activity

. Finally, we engineered a scratchpad-targeting gRNA expressed from a Wnt-

regulated promoter

(Methods), to enable both external control as well as recording of Wnt

pathway activity.

Using this cell line, we verified that smFISH could detect scratchpad collapse. After 48 h of

Cas9 and gRNA induction, we observed a substantial loss of scratchpad smFISH signal, but

not barcode signal (Fig. 2a, b, Extended Data Fig. 2). By contrast, in cells in which

MEMOIR recording was not induced, co-localization between barcode and scratchpad

signals was observed in approximately 90% of the transcripts, consistent with expected

smFISH accuracies

(Fig. 2b, c). Although individual barcoded scratchpad transcripts

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appeared either collapsed or uncollapsed based on co-localization, cells typically exhibited a

mixture of collapsed and uncollapsed scratchpads with the same barcode owing to the

existence of multiple genomic integrations undergoing independent collapse events

(Extended Data Fig. 1b). Together, these results indicate that scratchpad states can be altered

and that the fraction of collapsed scratchpads for each barcode can be subsequently read out

in situ

The fraction of collapsed scratchpads increased progressively over time after Cas9 and

gRNA induction, as required for MEMOIR operation. We observed an approximately 27%

decrease in mean co-localization fraction after 48 h of Cas9 and gRNA induction (Fig. 2b,

c). Additionally, the collapse rate correlated with the level of gRNA expression, suggesting

that collapse rates are tuneable (Extended Data Fig. 2d). By contrast, in the absence of

induction, scratchpad states remained stable (Extended Data Fig. 2e–g). Further, a Cre-

activated gRNA functioned similarly to the Wnt-activated gRNA (Extended Data Fig. 3a–d),

and scratchpad collapse also occurred in CHO-K1 cells and budding yeast (Extended Data

Fig. 3e, f), suggesting that the system design can be generalized to other methods of

activation and to other species. Finally, we verified that seqFISH could enable readout of 13

distinct barcoded scratchpads in single cells using 7 rounds of hybridization (Fig. 2d, e;

Methods).

To analyse cell lineage, we activated MEMOIR and allowed cells to grow for 3 or 4

generations, while performing time-lapse imaging to establish an independent ‘ground truth’

lineage for later validation (Fig. 3a). We then fixed the cells and analysed their barcoded

scratchpads by seqFISH (Fig. 3b). Altogether, we analysed 108 colonies, including 836

cells.

Inspection of scratchpad collapse patterns revealed lineage information. For example, in one

colony, barcode 9 was differentially collapsed between two 4-cell clades, consistent with a

collapse event occurring after the first cell division (Fig. 3c, left). Similarly, barcode 2

revealed distinct collapse frequencies between first cousins, but similar frequencies between

sister cell pairs (Fig. 3c, middle). Barcode 10 provided additional lineage information, as

different sister cell pairs showed collapse frequencies that were similar to each other but

different from their cousins (Fig. 3c, right). These examples, along with others (Extended

Data Figs 4 and 5), show how scratchpad collapse patterns can provide insight into lineage

relationships.

To analyse lineage reconstruction more systematically, we tabulated scratchpad collapse

frequencies for all probed barcodes in each colony (Fig. 3d) and used these data to calculate

a cell-to-cell ‘distance’ matrix, representing differences in collapse patterns between each

pair of cells (Fig. 3e; Supplementary Information). We then applied a binary hierarchical

clustering algorithm adapted from phylogenetic analysis to these distance scores in order to

reconstruct a lineage tree

(Fig. 3f; see Methods). Finally, as validation, we compared

each reconstructed tree to the actual colony lineage obtained directly from the corresponding

time-lapse video (Fig. 3a).

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Across all 108 colonies, we observed a broad distribution of reconstruction fidelity (Fig. 3g,

all colonies). However, using a bootstrap procedure to rank colonies based on the robustness

of reconstruction to resampling of the underlying data, it was possible to identify colonies

with more informative scratchpad collapse patterns, and these tended to reconstruct with

higher accuracy (Extended Data Fig. 6; Methods). For example, within the top 20% of

colonies ranked by bootstrap, 72% of lineage relationships were correctly reconstructed

(Fig. 3g, subset 1 and Extended Data Fig. 6a).

To compare these results to theoretical expectations, we simulated idealized MEMOIR

operation in three-generation binary trees (Methods). As expected, mean reconstruction

fidelity increased with the number of distinct scratchpads and required relatively few

scratch-pads to reach high fidelity. For example, fidelity was

−29

+19

(mean and 68% central

confidence interval) for 10 scratchpads and

−8

for 20 scratchpads at the experimentally

measured collapse rate of approximately 0.1 per scratchpad per cell generation (Fig. 3h).

With around eight scratchpads, the performance of these idealized simulations matched that

of the bootstrap selected colonies (Fig. 3g, Extended Data Fig. 6b, subset 1), consistent with

the majority of the 13 barcoded scratchpads targeted by seqFISH providing useful

information. The diversity of states generated corresponds to approximately 2

= 256

scratchpad configurations, comparable to the number of distinguishable alleles observed by

sequencing-based approaches

The current implementation of MEMOIR exhibited limited reconstruction depth and

accuracy. To understand the relevant sources of error, we performed more detailed

simulations, incorporating empirical measurements of noise in both recording (Cas9 and

gRNA expression) and readout (for example, scratchpad expression and smFISH detection)

(Extended Data Fig. 7). Notably, stochasticity in Cas9 and gRNA expression, as well as

smFISH detection, contributed relatively minor errors in reconstruction. Rather, for a given

number of scratchpads, the primary sources of error in reconstruction were stochastic

fluctuations in scratchpad expression, and ambiguities introduced due to multiple

incorporations of the same barcoded scratchpad (Fig. 3i, Extended Data Fig. 7;

Supplementary Information). On the basis of this analysis, future versions of MEMOIR can

be improved by increasing the number of unique scratchpad variants and reducing noise in

their expression (see Supplementary Information for further discussion of potential

improvements). These improvements should enable MEMOIR to reconstruct deeper and/or

more sparsely sampled trees (Extended Data Figs 8 and 9).

Because MEMOIR is compatible with same-cell measurements of endogenous gene

expression through additional rounds of smFISH, it can provide both lineage and endpoint

cell state information for the same colony. This combination can provide insight into the

dynamics of switching between gene expression states (Fig. 4a). For example, ES cells

stochastically transition among states with distinct expression levels of the pluripotency

regulator

Esrrb

. To infer the rates of these transitions, we measured

Esrrb

expression,

and assigned each cell a probability of being in a high or low

Esrrb

expression state

(Fig. 4b; Supplementary Information). Using the MEMOIR-inferred lineage, we found that

sisters or first cousins were significantly more likely to appear in the same

Esrrb

expression

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state compared with pairs of second cousins (

< 0.004) (Fig. 4c, d). Using a dynamic

inference framework

, we further inferred the quantitative rates of switching between

states (Fig. 4d, right panel, and Extended Data Fig. 10; Supplementary Information), and

verified that they were consistent with direct measurements of switching dynamics

. Going

forward, multiplexed

in situ

transcriptional profiling of endogenous genes

, together with

MEMOIR, should enable analysis of more complex dynamic cell state transition processes.

The design of MEMOIR provides a platform that can record and read out histories of

dynamic cellular events beyond lineage information (Fig. 4e, f). Specifically, orthogonal

gRNAs expressed from signal- specific promoters can in principle record multiple

intracellular signals onto distinct sets of scratchpads. We simulated binary trees of six

generations in which different cell lineages experienced distinct time courses of two input

signals (Fig. 4g). In these simulations, one gRNA variant was constitutively expressed solely

to enable lineage reconstruction using one set of scratchpads. In addition, each of the signals

activated expression of a corresponding gRNA variant, generating collapse events in its own

specific set of 50 scratchpads, at a rate proportional to the signal magnitude. By analysing

endpoint scratchpad collapse patterns for all three sets of scratchpads, we were able to

reconstruct both lineage trees and event histories (Fig. 4e–g; Methods). This reconstruction

process takes advantage of the reconstructed lineage tree to map the most likely assignment

of collapse events from the signal-recording gRNAs to specific positions on the lineage tree,

with a maximum possible time resolution of one cell cycle (since the sequence of collapse

events within a cell cycle cannot be distinguished). Thus, over timescales of multiple cell

cycles, MEMOIR should enable analysis of the sequence, duration, and magnitude of signals

along individual cell lineages (Fig. 4g).

Using genomic DNA as a writable and readable recording medium within living cells is a

long-standing goal of synthetic biology

–

. A key application for this technology is to

enable analysis of lineage and molecular event histories that unfold in complex and optically

inaccessible developmental systems over timescales of multiple cell generations. MEMOIR

provides a proof of principle, showing recording and readout of such information with

endpoint single-cell

in situ

measurements. Importantly, the capacity of MEMOIR can be

extended beyond the current demonstration using more scratchpads with improved designs

and highly multiplexed seqFISH

. Thus, we anticipate this approach will open up new

ways of studying developmental trajectories in developing embryos, tumours, and other

systems, eventually enabling us to read, within their native spatial contexts, each cell’s own

individual ‘memoir’.

METHODS

Data reporting.

No statistical methods were used to predetermine sample size. The experiments were not

randomized and the investigators were not blinded to allocation during experiments outcome

assessment.

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MEMOIR component construction.

The scratchpad transposon was constructed from a ten-repeat array (20X PP7 stem loops)

derived from plasmid pCR4–24XPP7SL

and ligated directionally using BamH1 and BglII

sites into a modified form of the PiggyBac (PB) vector PB510B (SBI) lacking the 3

′

insulator and including a multiple cloning site (MCS). The CMV promoter was then

removed using NheI and SpeI and replaced by a PGK promoter with Gibson assembly. A

gBlock (IDT) containing the AvrII and Xhol restriction sites, priming sequences, and the

BGH polyA was then introduced 3

′

of the PP7 array by Gibson assembly using the EagI

site in the backbone. Unique barcodes were then inserted into the transposon in the region 3

′

of the scratchpad array either by Gibson assembly or directed ligation using AvrII and XhoI.

A total of 28 unique barcode sequences (Supplementary Table 1, GenScript Biotech) derived

from

Saccharomyces cerevisiae

were used to generate the barcoded scratchpads. Scratchpad

transposons were found to produce transcripts with half-lives of approximately 2 h

(Extended Data Fig. 1e–g).

The Cas9 construct was made using hSpCas9 from pX330

. First, the FKBP degron (DD)

was PCR-amplified from pBMN FKBP(DD)-YFP

and introduced with Gibson assembly

into pX330 restricted with AgeI, 5

′

of the open reading frame of hSpCas9, to create pX330-

DD-hSpCas9. DD-hSpCas9 was amplified from this plasmid by PCR and introduced into

another plasmid, 3

′

of a PGK promoter using Gibson assembly. After sequence verification,

the PGK-DD-hSpCas9 construct was excised using restriction enzymes (AvrII and SacII),

blunted with T4 polymerase, and ligated into a modified form of the PiggyBac vector

PB510B (SBI) lacking the CMV promoter and including a MCS. A non-transposon version

of Cas9 was also created using hSpCas9 amplified from pX330 and introduced with Gibson

assembly at the 3

′

end of a CMV promoter containing two Tet operator sites into a standard

plasmid backbone.

The Wnt-pathway-responsive gRNA expression transposon was created using a LEF-1

response element

. The enhancer and promoter combination exhibited low basal activity,

large dynamic range, and responsiveness to the GSK3 inhibitor CHIR99021 and the Wnt3a

ligand. This Wnt sensor was cloned upstream of a nuclear localization signal (NLS)-tagged

mTurquoise2, which served as a reporter of guide expression, that contained an embedded

gRNA. The gRNA was flanked by self-cleaving ribozymes to excise it from the mRNA

and was purchased as a gblock (IDT) and inserted using Gibson assembly between the end

of the mTurquoise2 coding sequence and a SV40 polyA. This construct was contained in a

modified form of the PiggyBac vector PB510B.

The Cre-activated gRNA expression transposon was created using the U6 TATA-lox

promoter design

, as illustrated (Extended Data Fig. 3a). The promoter, shRNA against

mTurquoise2, and gRNA regions were purchased as a gblocks or oligos (IDT) and inserted

into a modified form of the PiggyBac vector PB510B containing PGK-H2B-mTurquoise2.

Cell line engineering and culture conditions.

To create MEM-01 we co- transfected the E14 mouse embryonic stem cell line (ATCC cat

no. CRL-1821) with expression plasmids for-hSpCas9 and the Tet repressor and then

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selected on neomycin. A single Cas9-positive clone was then used for co-transfection of 28

PB transposon barcoded scratchpads and a PB transposon PGK-palmitoylatedmTurquoise2/

HygroR to facilitate segmentation of cell membranes and selection on hygromycin.

Subsequent scratchpad-containing clones were inspected for overall scratchpad expression

by smFISH. Scratchpad clones were also assessed for Cas9 expression, which was found to

be very low and heterogeneous in most clones, with no expression in many cells (for

example, 6 ± 21 transcripts per cell). A scratchpad clone with good scratchpad expression

was then simultaneously transfected with the DD-hSpCas9 PB transposon (to improve Cas9

expression (26 ± 17 transcripts per cell)) and the Wnt-activated gRNA expression PB

transposon. Cells were selected on blasticidin. Single clones were assessed for activation

potential on the basis of mTurquoise2 expression in response to CHIR99021 (Stemgent) or

Wnt3a (1324-WN-002 R&D systems), and enhanced Cas9 expression was m easured by

smFISH. Among these clones was MEM-01, which demonstrated good gRNA activation in

response to Wnt3a and increased Cas9 activity in the presence of the stabilizing agent,

Shield1 (Clontech) (Extended Data Fig. 2c). MEM-01 resembled the parental E14 line in

terms of cell morphology, cycle times, and expression of pluripotency markers including

Esrrb

Nanog

, and SSEA-1. Stably selected MEMOIR lines containing a Cre-activated

gRNA were similarly engineered (Extended Data Fig. 3a–d).

The transfections described above were carried out using Fugene HD (Promega) at a mass

(μg) DNA/volume (μl) Fugene ratio of 1:3 and following the manufacturer’s instructions.

For transfection of the PB components a total DNA mass of 1μg was used at a ratio of 6:1,

PB transposons to PB transposase PB200PA-1 (SBI). For selection with antibiotics,

transfected cells were lifted with Accutase (ThermoFisher) after transfection media was

removed and plated on 100-mm plates (Nunc). 24 h later growth media was replaced with

selection media. Single colonies were lifted from selection plates as they matured.

During standard cell culturing, ES cells were maintained at 37 °C and 5% CO

in GMEM

(Sigma), 15% ES cell qualified fetal bovine serum (FBS) (Gibco/ThermoFisher), PSG (2

mM L-glutamine, 100 units per ml penicillin, 100 μg ml

−1

streptomycin) (ThermoFisher), 1

mM sodium pyruvate (ThermoFisher), 1,000 units per ml Leukaemia Inhibitory Factor (LIF,

Millipore), 1 ×Minimum Essential Medium Non-Essential Amino Acids (MEM NEAA,

ThermoFisher) and 50–100μM

- mercaptoethanol (Gibco/ThermoFisher). Cells were

maintained on polystyrene (Falcon) coated with 0.1% gelatin (Sigma).

Quantitative PCR.

For detection of genomic barcode copy number, genomic DNA was prepared from cells

using the DNeasy Blood and Tissue kit (Qiagen). DNA was quantified on a NanoDrop 8000

spectrophotometer (ThermoScientific). Reactions were assembled as above with around

1,000–5,000 haploid genome copies, based on 3 picograms per haploid genome

approximation. For gene expression analysis, total RNA was prepared using the RNeasy

Mini kit (Qiagen). One microgram of total RNA was used with the iScript cDNA synthesis

kit (BioRad) following the manufacturer’s instructions. For qPCR a 1:20 dilution of the

cDNA was used in each reaction. All reactions were performed with IQ SYBR Green

Supermix (BioRad). Reaction cycling was carried out on a BioRad CFX96 thermocycler.

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Both genomic DNA and cDNA samples were compared against

Sdha

copy n umber or

expression level, respectively. Analyses included at least three biological replicates with

each reaction run in triplicate, unless otherwise noted. Primer sets for all barcodes and

normalizers were obtained from IDT, and the efficiencies of all primer pairs were tested.

Time-lapse videos and cell culture for imaging.

Tissue culture grade glass bottom 24-well plates (MatTek) were treated with laminin-511

(20 μg ml

−1

) (Biolamina) for 4 h at 37 °C and plated with cells at approximately 2,500 cells

per cm

. Cells were exposed to Wnt3a (50–100 ng ml

−1

) and Shield1 (50–100 nM) at the

time of plating. After approximately 16 h, cells were selected for time-lapse imaging based

on system activation, assessed by visible mTurquoise2 signal, and then imaged in an

incubated microscope environment every 14 min over 20–40 h before being immediately

fixed. Samples were fixed with 4% formaldehyde in PBS for 5 min. Samples cultured for

smFISH imaging, but without time-lapse video tracking, were prepared similarly (typically

with a higher plated cell density) and activated for different lengths of time, as stated.

Single molecule fluorescence

in situ

hybridization (smFISH).

Hybridization and imaging were carried out as previously described

with the following

exceptions: scratchpad transcripts were targeted with 40 DNA oligo 20mer probes and

barcode regions were targeted with 18 20mer probes (Supplementary Table 2). Probes were

coupled to one of three dyes (Alexa 555, 594 or 647 (ThermoFisher)) and used at

approximately 130 nM concentration per probe set. Post-hybridization, cells were washed in

20% formamide in 2× SSC containing DAPI at 30 °C for 30 min, rinsed in 2× SSC at room

temperature, and imaged in 2× SSC. For seqFISH, after imaging each round of

hybridization, 2× SSC was replaced with wash buffer for about 5 min at room temperature

and then replaced with the next probe set in hybridization buffer for overnight incubation.

Most barcode signals from the previous hybridization were no longer visible during imaging

of the following hybridization (owing to photobleaching and probe loss facilitated by the

small number of barcode probes (18) used per barcode); any remaining visible transcripts

were computationally subtracted during analysis. Incubation, washing, and imaging

proceeded as above for up to nine rounds of hybridization.

For analysis of smFISH images, semi-automated cell segmentation and dot detection were

performed using custom Matlab software. Raw images were processed by a Laplacian of the

Gaussian filter and then thresholded to select dots. Co-localization between dots in the

scratchpad image and barcode image was detected if both dots were above the threshold and

within a few pixels of each other. To generate the histogram of intensities for the collapsed

and uncollapsed scratchpads in Fig. 2b, we integrated the fluorescence intensities in the

regions of the scratchpad smFISH image that corresponded to individual barcode dots or the

detected scratchpad dots, respectively. For the collapse rate experiment in Fig. 2c and

Extended Data Fig. 3c, we measured the aggregate smFISH scratchpad co-localization levels

for four highly expressed barcodes in cells that had been induced for different lengths of

time. For activating conditions shown in Fig. 2b, c, only data from cells that were actually

activated (as assessed by mTurquoise2 expression) were included.

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Lineage reconstruction of experimental data.

Cell-to-cell barcode distance scores were determined for each pair of cells based on the

similarity of the two cells’ co-localization fractions for each barcode and weighted by the

barcode’s transcript number (as a measure of confidence in the observation). See

Supplementary Information for details.

Lineage trees were reconstructed from the cell-to-cell barcode distance matrices using a

modified version of a standard agglomerative hierarchical clustering algorithm

Reconstructions were constrained to binary trees such that cells were paired into sisters

before first cousin pairs were assigned. Pairing proceeded by successively grouping pairs of

cells or cell clusters with the minimum barcode distance. At each step, if the two most

optimal (that is, minimum distance) pairings were close in distance, the algorithm optimized

for the lowest combined distance of the current and next minimum distances. The distance

between two clusters was computed using the standard UPGMA algorithm

by averaging

the cell-to-cell barcode distance between all possible pairs of cells across the two clusters.

Bootstrap to identify robust reconstructions.

For each colony, the barcoded scratchpad data were resampled by bootstrap and

corresponding lineage trees were reconstructed (

= 1,000 resampled reconstructions per

colony). On the basis of the frequency at which the original cousin clades occurred in the

resampled reconstructed trees, a robustness score was assigned to each colony. Colonies

whose clade reconstructions were less sensitive to resampling showed significantly

improved overall reconstruction accuracy. Subsets of colonies with more reliable

reconstructions could thus be selected without prior knowledge of their accuracy by

selecting colonies with higher robustness scores, for example, scores in the top 20–40% of

the data.

Alternative metrics for identifying colonies with robust lineage information were also tested.

These metrics similarly enriched for subsets of data with improved reconstruction accuracy,

further supporting the observation that some colonies showed clear lineage information

while others did not acquire well-defined collapse patterns, probably owing to limited,

excessive, or ambiguous collapse events.

Lineage reconstruction simulations.

To simulate MEMOIR for three-generation binary trees, we started with one cell with a fixed

number of idealized scratchpads. At each division, the daughter cells inherited the same

scratchpad profile as their parent and independently collapsed each uncollapsed site with a

fixed probability, defined as the collapse rate. After three generations, the scratchpad profiles

of the eight resulting cells were used to reconstruct their lineage tree using either a modified

neighbour joining algorithm

, or the Camin–Sokal maximum parsimony algorithm

that

exhaustively scored all 315 possible tree reconstructions. Both forward simulations and the

reconstruction algorithms were implemented in Matlab. For the heat map and the cumulative

distribution functions shown in Fig. 3g–i, the fraction of correct relationships was computed

as the fraction of all distinct pairwise relationships in the actual tree that were correctly

identified in the reconstructed tree. If multiple reconstructions were equally valid (same

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parsimony score), the fraction of correct relationships was averaged over all of them.

Reconstruction accuracy was tested over a wide range of collapse rates (Fig. 3h) or for the

approximate collapse rate observed in our experiments, 0.1 per site per generation (Fig.

3g,i). The empirical collapse rate, 0.1, was estimated from the observed co-localization

fraction of the barcodes, ~0.67, in 108 MEM-01 colonies induced for approximately 48 h

(same colonies as in Fig. 3). In Extended Data Fig. 8a, trees of a higher number of

generations were reconstructed from the final collapse pattern using a modified neighbour

joining algorithm

in which allowed reconstructions were restricted to full binary trees.

Fraction of correct relationships was again computed as the fraction of all distinct pairwise

relationships in the actual tree that were correctly identified in the reconstructed tree

averaged over at least 1,000 trees.

Event recording simulations.

Simulation of signal recording.—

To demonstrate event recording, we simulated the

same forward tree-generation algorithm as in the MEMOIR lineage reconstruction

simulations (Fig. 3h and Methods), for trees of six generations, assuming 50 idealized

scratchpads and a collapse rate of 0.1 per scratchpad per generation. The simulated cells also

contained two additional sets of recording scratchpads of 50 sites each (Fig. 4e). We

assumed these scratchpads collapsed through independent events occurring at rates

proportional to the magnitude of their respective input signals. The minimum and maximum

collapse rates at low and high signal were set to 0 and 0.2 per scratchpad per generation,

respectively. The magnitude of the input signals varied over time and from branch to branch

as shown in Fig. 4f, g, resulting in different collapse rates for each of the two recording

scratchpad sets over time and along different lineages.

Reconstruction of simulated signal dynamics.—

We first reconstructed the lineage

tree using only the lineage-tracking scratchpad sites. This reconstruction used a neighbour-

joining algorithm, as in Fig. 3h

. We then reconstructed the history of the collapse events

of the recording scratchpads on the reconstructed lineage tree. For this procedure, we used a

Camin–Sokal maximum parsimony algorithm

. In brief, the algorithm proceeds from the

leaves of the tree to the root. At each generation, it infers the collapse state of the parental

node, based on the known collapse states of the two daughters, while minimizing the number

of new collapse events occurring between the parent and the daughters. For binary

scratchpads this corresponds to computing the intersection between the collapse patterns of

the two daughters. This procedure is then repeated for the parent and its sister until reaching

the root. At the end of this procedure, one obtains a maximum parsimony assignment of

scratchpad states to each node in the tree. On the basis of these assignments, we calculated

the number of scratchpad collapse events in recording scratchpads that occurred along each

branch. Finally, this reconstructed collapse level provides an estimate of the underlying

signal intensity along each lineage (for example, actual and reconstructed signals shown for

two lineages of interest in Fig. 4g).

Data availability.

Data that are not included in the paper are available upon reasonable request to the authors.

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Extended Data

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Extended Data Figure 1 |. MEM-01 consistently expresses short-lived transcripts from multiple

integrated barcoded scratchpads.

, The barcoded scratchpad transposon is composed of the following elements (left to right):

the PiggyBac 5

′

terminal repeat (triangle), the chicken HS4 insulator

, a PGK promoter

driving expression of the hygromycin resistance coding sequence, a 5

′

FRT site, the PP7

scratchpad array consisting of 10 repeats, a 3

′

FRT site, a barcode sequence (Supplementary

Table 1), a priming region for sequencing and PCR, the BGH polyA, and the PiggyBac 3

′

terminal repeat (triangle).

, Unique genomic integrations for the MEM-01 cell line were

detected by qPCR. Bars show mean ± s.d. of four biological repeats with individual data

points marked.

, The relative RNA expression levels of barcode integrations were

quantified by RT–qPCR. Bars show mean ± s.d. of three biological repeats with individual

data points marked.

, Scratchpad expression profiles remain constant over 1.3 months of

passaging. Low- and high-passage cultures of MEM-01 cells (light and dark bars,

respectively) were assayed for RNA expression levels by RT–qPCR. The unchanged

expression levels indicate that most barcoded scratchpads express at a consistent level and

are not routinely silenced over time. Bars show values from single biological samples with

error bars calculated by combining in quadrature the technical replicate variation in barcode

and normalizer quantitation cycle, Cq, values.

–

, RNA half-lives assessed by RT–qPCR

analysis of transcript levels after blocking transcription with actinomycin D (10μg ml

−

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Barcoded scratchpad transcripts were assayed with two different sets of qPCR primers (left

and right panels). These data indicate a half-life of approximately 2 h.

Myc

and

Sdha

are known to have short and long mRNA half-lives, respectively, and were assessed as

controls, for comparison

–

Myc

half-life (

) of 1 h was shorter than the other measured

half-lives, while

Sdha

(

) was longer lived. For

Sdha

, the measured half-life value (indicated

with an asterisk) is expected to overestimate the true value, as

Sdha

levels were determined

relative to those of the similarly long-lived gene

Atp5e

, whose transcript levels were also

decaying over the time course. A previous estimate of

Sdha

half-life in mESCs was 8–13 h

(ref.

). All sample transcript levels were assessed relative to those of

Atp5e

–

Transcript abundances were normalized to 1 at time zero. Decay curves were fit assuming

one-phase exponential decay using weighted nonlinear least squares regression (

) or

assuming a linear approximation to exponential decay (

). Half-lives were determined on the

basis of the best fit decay constants and a range reported based on the 95% confidence

interval (shown in parentheses). Data represent two biological replicates with multiple

technical replicates; error bars show standard deviations.

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Extended Data Figure 2 |. Barcoded scratchpads collapse to truncated products in activated cells

and are stable in full-length and collapsed forms.

, Agarose gel electrophoresis of PCR amplified scratchpads reveals scratchpad collapse

after gRNA induction. Full-length scratchpads were amplified from plasmid DNA (lane 1),

as well as from cells without gRNA constructs (lane 3), or with uninduced gRNAs (lane 4).

By contrast, cells expressing gRNA showed shorter products (lane 5). Cells with no

scratchpads are also shown as a negative control (lane 2). Bands corresponding to the full-

length scratchpad and the collapsed scratchpad are indicated (arrows). Note that the

laddering effect seen in all lanes and gels is due in part to PCR amplification artefacts with

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the repetitive arrays. For gel source data, see Supplementary Fig. 1.

, The lowest molecular

weight band from scratchpad collapse, as shown in lane 5 in

, was extracted and subcloned

into a vector. Nine of the colonies were sequenced. They aligned to a single repeat unit with

′

and 3

′

flanking regions, suggesting complete collapse of the repeats owing to Cas9

activity. Six of the nine sequencing reads resulted in collapse to a perfect single repeat (with

a possible point mutation in the scratchpad sequence associated with barcode 2), and the

remaining three sequencing reads had additional small deletions in the scratchpad.

Scratchpad collapse requires induction of both Cas9 and gRNA. The gel shows scratchpad

states for MEM-01 cells treated with no ligand, with Shield1 (to stabilize Cas9 protein), with

Wnt3a (to induce gRNA expression), and with both Wnt3a (100 ng ml

−1

) and Shield1 (100

nM), all after 48 h.

, Scratchpad collapse increased with increasing gRNA activation, as

assessed using smFISH to detect scratchpad co-localization with four highly expressed

barcodes. Cells were analysed either without gRNA activation or 48 h after gRNA activation

by addition of Wnt3a and Shield1 (same concentrations as in

). gRNA expression was

measured by the intensity of co-expressed nuclear mTurquoise signal. Box plots show

median (red bar), first and third quartiles (box), and extrema of distributions;

= 1,826,

1,081, 345, 191 cells, left to right. Related to Fig. 2c.

–

, Scratchpad states remain stable

over extended periods.

, Unactivated MEM-01 cells maintained uncollapsed scratchpads

over timescales of months.

, To check the stability of individual barcoded scratchpad

variants over time, multiple subclones of MEM-01 were isolated after no activation (control;

top panels) and after a pulse of activation for 24 h (Wnt3a 100 ng ml

−1

, Shield1 100 nM;

bottom panels). Subclones were assessed for the states of different barcoded scratchpad

types after initial isolation (0 month relative age, left) and after one month of maintenance

(right). The apparent collapse states (from uncollapsed to fully collapsed) of the barcoded

scratchpad types were distinct in different subclones and remained stable over a month,

indicating that scratchpad states are stable over these timescales.

, Barcoded scratchpads

are also stable over long periods as assessed by smFISH readout. The fraction per cell of

barcode transcripts (from four distinct barcode types) that co-localized with scratchpad

signal was essentially unchanged between an unactivated low passage cell culture and one

maintained for over a month. The imperfect co-localization fraction is largely the result of

errors in smFISH detection and not gradual scratchpad collapse. Boxplots as in

;

= 1,826,

983 cells, left to right.

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Extended Data Figure 3 |. Scratchpad collapse works with an alternative gRNA, and in multiple

cell types.

–

, A Cre-recombinase-activated gRNA is effective at inducing collapse events.

Schematic of Creactivated gRNA system. The construct contains a constitutive PGK

promoter driving expression of a histone 2B (H2B)–mTurquoise fusion protein (the H2B

provides nuclear localization). This is followed by a U6 TATA-

lox

promoter

driving

expression of an shRNA against mTurquoise, followed in turn by a polyT (T6)

transcriptional stop, and then a gRNA directed against scratchpad regions. Prior to Cre

expression, expression of the shRNA keeps mTurquoise levels low (brown dashed line) and

prevents expression of the gRNA. After the introduction of Cre, the shRNA-stop cassette is

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removed, allowing mTurquoise and gRNA expression. Thus, mTurquoise provides a visual

marker of gRNA expression. This type of gRNA architecture could allow MEMOIR

activation in specific tissues expressing Cre.

, PCR analysis shows that Cre can induce

scratchpad collapse. Gel shows genomic DNA from a clonal cell line harbouring the

construct in

. Scratchpads appear uncollapsed in untransfected cells (left lane), but show

significant collapse after transfection with mRNA encoding Cre protein (right lane,

approximately 52 h after transfection). Note that the laddering effect seen in all lanes and

gels is due in part to PCR amplification artefacts with the repetitive arrays.

, smFISH

analysis reveals Cre-activated scratchpad collapse. Quantification of barcode–scratchpad co-

localization fractions as measured by smFISH. Cre transfection reduced scratchpad and

barcode co-localization levels in cells that showed evidence of Cre activity, as assessed by

mTurquoise expression (right). Transfected cells that were mTurquoise-negative or low and

untransfected cells retained high co-localization levels (middle and left). Co-localization

levels per cell were assessed based on the co-localization of four expressed barcodes with

scratchpad transcripts. Box plots show median (red bar), first and third quartiles (box), and

extrema of distributions;

n =

995, 643, 649 cells, left to right.

, Example smFISH images of

scratchpad and barcode co-localization detected in single cells containing the Cre-activated

gRNA. Some activated cells (top panels, mTurquoise expression ‘on’) show loss of co-

localized signal for a specific barcode (top panels, lower cell). Unactivated cells, as assessed

by low mTurquoise expression, typically show no loss of co-localization (bottom panels).

Scale bars, 10 μm.

, Scratchpads in CHO-K1 cells and yeast also undergo Cas9/gRNA-

dependent collapse.

, Cas9- and gRNA-expressing plasmids were transiently transfected

into Chinese Hamster Ovary (CHO-K1) cells containing stably integrated scratchpads. Gel

analysis reveals Cas9 and gRNA-dependent scratchpad collapse (middle lane), while

transfection with a Cas9-expressing plasmid alone or control plasmids resulted in no

collapse (left and right lanes, respectively).

, Scratchpad collapse was tested in a yeast strain

with doxycycline-inducible Cas9 and gRNA and integrated scratchpads. Before inducing

Cas9-gRNA expression (lane 1 and 3), the scratchpads were intact. After Cas9-gRNA

induction with 2 μg ml

−

doxycycline for 11 h, scratchpads appeared collapsed (lane 2 and

4). Left two lanes (lanes 1 and 2) and right two lanes (lanes 3 and 4) correspond to two

biological replicates. Note that the scratchpads in CHO-K1 and yeast cells have a similar

scratchpad PP7 array to that used elsewhere but different flanking sequences, so their

absolute PCR product lengths differ. For gel source data, see Supplementary Fig. 1.

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Extended Data Figure 4 |. Examples of lineage reconstruction for ten colonies.

Data for ten colonies that reconstructed with > 70% of pairwise relationships correctly

identified are shown here. The bubble chart shows the number of barcode transcripts

detected (bubble size) and the uncollapsed fraction (colour scale). Matrix of cell-to-cell

barcode distance (dissimilarity) scores were computed from the data. Low (blue) values

indicate more similar barcoded scratchpad collapse patterns. Note that sisters and cousins

tend to have lower distance scores than second cousins, creating a block diagonal pattern in

the distance matrix. Lineage trees were reconstructed based on the distance matrix using an

agglomerative hierarchical clustering algorithm (see Methods). Cluster distances from the

reconstruction algorithm are shown as branch heights in the reconstructed linkage trees.

Percentages on the linkage trees represent frequencies of clade occurrence from a barcode

resampling bootstrap. The percentage of correct relationships identified by the depicted

lineage reconstruction is shown as a percentage and the actual tree is reported as [(

x y

)(

x y

)]

[(

x y

)(

x y

)], where sister pairs are denoted as (

x y

) and cousins are grouped in brackets

([...]).

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