ArticleNature communications2020
Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
59 citing papers in PubMed.
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Article
- Patient-Derived Organoid-Based CRISPR Screens in Cancer Research: Applications, Advances, and Challenges.Cancer medicine · 2026Review
- LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.Bioinformatics (Oxford, England) · 2026Article
- Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining.Genome research · 2026Article
- Charting single-cell lineages with synthetic and natural barcodes.Nature reviews. Genetics · 2026Review
- A comparison between CARLIN and DNA Typewriter in CRISPR-mediated lineage tracing.BMC bioinformatics · 2026Article
- MiTo: tracing the phenotypic evolution of somatic cell lineages via mitochondrial single-cell multi-omics.Nature communications · 2026Article
- PSMB1 screened by CRISPR-CAS9 promoted breast cancer progression via PI3K-AKT-mTOR signaling.Discover oncology · 2026Article
- Modern Mining: The Role of Single-cell RNA Sequencing in Advancing Neuroscience Research.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.bioRxiv : the preprint server for biology · 2026Article
- Inferring cell differentiation maps from lineage tracing data.Nature methods · 2026Article
- Integrative Inference of Spatially Resolved Cell Lineage Trees using LineageMap.bioRxiv : the preprint server for biology · 2026Article
- Cell lineage tracing: Methods, applications, and challenges.Quantitative biology (Beijing, China) · 2025Review
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Article
- Maximum likelihood inference of time-scaled cell lineage trees with mixed-type missing data using LAML.Genome biology · 2025Article
- Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns.Nature communications · 2025Article
- Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research.Acta biochimica Polonica · 2025Review
- Multi-scale data reveal a CD24(+) MUCL1(+) tumor subgroup associated with unfavorable prognosis in ER+ breast cancer.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.