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CRISPR-based lineage tracing records a cell’s ancestry by cutting an experimentally introduced DNA barcode. Cas9 and guide RNAs create insertions, deletions, and other sequence changes; daughter cells inherit the changes made in their ancestors. Researchers sequence those scars in sampled cells and use shared mutations to infer a branching lineage tree. When the same cells also undergo single-cell RNA sequencing, the inferred ancestry can be related to each cell’s identity and gene-expression state. The barcode is measured evidence; the tree is a computational reconstruction with uncertainty.
What CRISPR lineage tracing actually records
CRISPR does not label ancestry automatically. The experiment first places a recording sequence—usually a designed barcode array or another reporter construct—inside the cells being studied. Cas9, guided by short guide RNAs, cuts selected sites in that sequence. DNA repair is variable, so different cells acquire different insertions, deletions, or other sequence alterations. Those alterations are the molecular “scars” that can later be read.
During the period when Cas9 is active, scars accumulate in different combinations. As a cell divides, its descendants inherit the scars already present in that cell, and some descendants may acquire additional edits. Burgess describes the process in Nature Reviews Genetics as follows: “During the transient period of Cas9 activity, the barcodes are cumulatively cut and repaired in variable ways in different cells.” The recorded sequence is therefore a history of editing events, not a video of every cell division. Burgess, 2018
How the method works, step by step
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Introduce a recording construct
Researchers engineer a model system so that cells carry a DNA barcode array or related recording sequence. The design determines which sites can be cut and how many independent marks can be accumulated. Because the marker is introduced experimentally, this is called prospective lineage tracing.
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Activate Cas9 and guide RNAs
Cas9 and guide RNAs target selected barcode sites. Each cut is repaired by the cell, often leaving a sequence change. Repair outcomes vary among cells, creating distinguishable combinations of scars rather than one identical label in every cell.
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Allow edits to be inherited and accumulate
When a marked cell divides, edits already present in its DNA are copied into daughter cells. Cas9 activity can add later edits in some descendants. Two cells with several shared scars are therefore more consistent with a recent common ancestor than cells sharing no informative edits, although the relationship is probabilistic.
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Sample cells at a defined time
At the chosen developmental or experimental time point, researchers collect cells or tissue. Only sampled cells and successfully recovered barcode reads contribute direct evidence; unsampled cells and unrecorded divisions remain unknown.
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Sequence the barcode regions
Sequencing reveals the scar pattern in each sampled cell. The data may include intact sites, deletions, insertions, and partially missing reads. Quality control is needed because sequencing errors, dropout, and incomplete recovery can make two genuinely related cells look less similar or create spurious differences.
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Infer a lineage tree computationally
Algorithms compare shared and distinct mutations across cells and search for a branching tree that explains those patterns. The resulting tree is an inference from the observed scars, not a directly observed object. Different trees can fit the same mutation data when marks are sparse, noisy, or saturated.
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Optionally profile cell state in the same cells
If barcode reads are paired with single-cell RNA sequencing, each cell has both an ancestry record and a transcriptomic profile. Researchers can then ask whether cells with a common inferred ancestor adopted the same cell type, diverged into different states, or followed distinct differentiation paths.
Measured barcode versus inferred tree
Keeping these two layers separate prevents overclaiming. The sequencer measures nucleotide changes in a barcode. Software converts those observations into relationships and branch points.
| Layer | What it contains | What it can support | What it cannot guarantee |
|---|---|---|---|
| Measured record | Observed insertions, deletions, intact sites, and read quality in each sampled cell | Evidence that cells share particular editing events | A complete record of every division or every unsampled intermediate |
| Inferred relationship | A computationally scored tree or set of candidate trees based on shared scars | Likely common ancestry and branching order among sampled cells | Certainty that one exact topology is the historical truth |
| Cell-state profile | Gene-expression measurements, often from single-cell RNA sequencing | Cell type, state, and expression differences associated with branches | A lineage tree independent of assumptions about expression and missing states |
What the method can reveal
Common progenitors and clonal branches
Shared scars can identify groups of sampled cells that probably descended from a common progenitor. Additional mutations help separate branches within that group. This is useful for asking whether a population arose from one or several founders and where branches split during development.
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Ancestry of cell types
Expression profiles tell researchers what a cell is or is doing now. A lineage record adds evidence about where it came from. Two mature cell types that look dissimilar transcriptionally may share an earlier barcode pattern, indicating a common ancestor; conversely, similar-looking cells can arise from separate lineages.
Development in model organisms
Prospective CRISPR barcoding has been demonstrated in model organisms rather than as a procedure for prospective marking of people. The LINNAEUS study combined CRISPR–Cas9-induced reporter scars with single-cell RNA sequencing to trace lineages while identifying cell types. It reconstructed developmental trees in zebrafish larvae and in adult fish heart, liver, pancreas, and telencephalon. Spanjaard et al., 2018
Potential differentiation paths
When a lineage branch contains cells in different expression states, investigators can examine how identity changes along that branch. The result can connect ancestry with a possible differentiation route, but it does not by itself prove that one measured state obligatorily transforms into another. Sampling time, missing intermediates, and model assumptions still matter.
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Why single-cell RNA sequencing adds value
Barcode-only experiments primarily answer “which cells are related?” Single-cell RNA sequencing adds “what is each related cell expressing?” The combination allows a study to map inferred branches onto cell types, activation states, or developmental programs. It can reveal whether a progenitor produces multiple identities, whether one identity comes from multiple branches, and where transcriptional divergence appears relative to a lineage split.
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Integration is computational rather than automatic. One approach, LinTIMaT, combines mutation and transcriptomic evidence to score candidate lineage trees, using expression information to help resolve ambiguities that mutation data alone leave open. Zafar, Lin and Bar-Joseph, 2020 Expression data can improve cell-type coherence, but trajectory methods also rely on assumptions about how expression changes over time and may not recover an intermediate cell that was never sampled.
Limits that shape the result
Editing efficiency and uneven recording
Not every target site is cut in every cell, and repair outcomes are not equally informative. A weakly edited barcode may contain too little shared history to distinguish close relatives. Conversely, highly active sites can change repeatedly and lose information about earlier events.
Saturation and convergent scars
Barcode capacity is finite. Once many sites have been edited, later events can overwrite or obscure distinctions, a problem known as saturation. Different cells can also acquire the same short deletion independently, so a shared scar is evidence whose strength depends on how likely that event is to recur and on the context of the other scars.
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Sequencing errors, allelic dropout, damaged material, and failed recovery can make the observed record incomplete. The tree describes sampled cells, not the entire organism. An apparent direct branch may hide several unsampled generations.
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Alternative trees and model assumptions
When mutation patterns are compatible with multiple topologies, software may return a best-scoring tree even though other trees remain plausible. Transcriptomic integration can constrain choices but introduces its own assumptions. Results should therefore report uncertainty, confidence or competing trees where available, rather than presenting every branch as certain.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How CRISPR barcoding compares with other lineage approaches
| Approach | Source of the mark | Typical information paired with ancestry | Strengths | Important limits |
|---|---|---|---|---|
| Prospective CRISPR barcoding | Experimentally introduced DNA construct edited by Cas9 and guide RNAs | Barcode sequencing; optionally single-cell RNA profiles | Designed recording capacity and direct pairing with cell identity in the same experiment | Requires engineering, can suffer from editing noise and saturation, and is generally demonstrated in model systems |
| Prospective recombinase barcoding | Experimentally introduced recombination sites and an activating recombinase | Barcode or reporter readout; cell-state assays can be added | Can mark cells prospectively with engineered rules | Resolution depends on the construct and recombination history; a universal performance level is not established |
| Retrospective somatic lineage tracing | Naturally arising somatic mutations used as endogenous marks | Genomic mutation profiles and, in some studies, transcriptomes | No engineered barcode is required; records mutations generated during normal biology | Mutation timing and rate are uncontrolled, and informative marks may be sparse or uneven |
| Expression-based trajectory inference | No physical ancestry mark; computational patterns in gene expression | Single-cell transcriptomic states | Can suggest developmental tracks without a recording construct | Relies on assumptions about expression similarity and time; a suggested trajectory is not direct ancestry evidence |
Reviews of lineage-tracing strategies emphasize comparing prospective versus retrospective marking, whether cell-state information is collected, scale, spatial readout, and the quality and saturation of recorded marks. Baron and van Oudenaarden, 2019 Recent work also surveys advances in prospective single-cell lineage barcoding and their applications. Genome Research, 2024
Design choices researchers must make
When to introduce and activate the barcode
The timing of construct delivery and Cas9 activity defines which generations can be recorded. Early activation can capture broad founder relationships but may create a large, complex record; later activation focuses on a narrower developmental window.
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More target sites and a suitable editing window can provide more combinations of scars, but excessive activity increases saturation and repeated edits. The construct must balance diversity with interpretable, recoverable reads.
When and where to sample
Sampling determines which branches are visible. A single endpoint can show relationships among surviving cells, while multiple time points can make developmental ordering easier to assess. Tissue dissociation may preserve barcode and transcriptome information but loses some spatial context unless a spatial readout is added.
How to validate the reconstruction
Researchers should inspect read quality, editing distributions, missing data, and the stability of branches under alternative computational settings. Cell-type assignments from transcriptomes should be checked independently of the lineage tree so that expression similarity is not mistaken for ancestry.
How to interpret a published lineage tree
- Check the marking strategy: identify whether the study used an introduced CRISPR construct, an endogenous mutation, or expression-only inference.
- Locate the direct measurements: determine which barcode sites and cells were actually sequenced and how much missing data were present.
- Separate branches from labels: a branch is inferred from mutation patterns; a cell-type label comes from expression or other phenotype data.
- Look for uncertainty: read confidence scores, alternative trees, bootstrap or posterior support when reported, and statements about saturation or dropout.
- Limit the claim to the sample: a reconstructed relationship among sampled cells does not establish the ancestry of every cell in the tissue or organism.
What CRISPR lineage tracing cannot establish by itself
The method cannot provide a continuous movie of development, guarantee that every shared mutation arose only once, or prove a causal differentiation mechanism from a tree alone. It also cannot recover cells that were never sampled. Pairing barcodes with transcriptomes makes lineage and identity easier to relate, but it does not remove the assumptions of either tree reconstruction or expression-based trajectory analysis. The scientifically careful conclusion is usually that a branch is supported, likely, or consistent with a developmental relationship—not that every inferred division is directly observed.
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