Knowing the ancestral states and lineage relationships of individual cells could unravel the dynamic programs underlying development. Engineering cells to actively record information within their own genomic DNA could reveal these histories, but existing recording systems have limited information capacity or disrupt spatial context. Here, we introduce baseMEMOIR, which combines base editing, sequential hybridization imaging, and Bayesian inference to allow reconstruction of high resolution cell lineage trees and cell state dynamics while preserving spatial organization. BaseMEMOIR stochastically and irreversibly edits engineered dinucleotides to one of three alternative image-readable states. By genomically integrating arrays of editable dinucleotides, we constructed an embryonic stem cell line with 792 bits of recordable, image-readable memory. Simulations showed that this memory size was sufficient for accurate reconstruction of deep lineage trees. Experimentally, baseMEMOIR allowed precise reconstruction of lineage trees 6 or more generations deep in embryonic stem cell colonies. Further, it also allowed inference of ancestral cell states and their quantitative cell state transition rates, all from endpoint images. baseMEMOIR thus provides a scalable framework for reconstructing single cell histories in spatially organized multicellular systems.
", "doi": "10.22002/327t7-ke088", "publisher": "CaltechDATA", "publication_date": "2023-12-12" }, { "id": "data:kn8yx-kmb24", "collection": "data", "collection_id": "kn8yx-kmb24", "cite_using_url": "https://data.caltech.edu/records/kn8yx-kmb24", "type": "collection", "title": "Lineage motifs: developmental modules for control of cell type proportions (post-revision)", "author": [ { "family_name": "Tran", "given_name": "Martin", "orcid": "0000-0001-9882-7230" }, { "family_name": "Askary", "given_name": "Amjad" }, { "family_name": "Elowitz", "given_name": "Michael B.", "orcid": "0000-0002-1221-0967" } ], "abstract": "In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells or extrinsic interactions that produce specific patterns of descendant cell types on lineage trees. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer progenitor states and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states or extrinsic interactions. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in retina and early embryonic development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.
", "doi": "10.22002/kn8yx-kmb24", "publisher": "CaltechDATA", "publication_date": "2023-10-10" }, { "id": "data:hf6zq-zmg82", "collection": "data", "collection_id": "hf6zq-zmg82", "cite_using_url": "https://data.caltech.edu/records/hf6zq-zmg82", "type": "collection", "title": "Combinatorial expression motifs in signaling pathways", "author": [ { "family_name": "Granados", "given_name": "Alejandro A.", "orcid": "0000-0002-6275-9800" }, { "family_name": "Kanrar", "given_name": "Nivedita", "orcid": "0000-0003-0047-951X" }, { "family_name": "Elowitz", "given_name": "Michael B.", "orcid": "0000-0002-1221-0967" } ], "abstract": "This deposit contains the raw and processed data and documents for the article, 'Combinatorial expression motifs in signaling pathways.' Code and analysis (organized by figure) are available at the GitHub repository https://github.com/nkanrar/motifs and Caltech DATA https://data.caltech.edu/records/bgm15-18g17. Please see the related publication for more details, and contact the corresponding authors with any questions.
", "doi": "10.22002/hf6zq-zmg82", "publisher": "CaltechDATA", "publication_date": "2023-08-02" }, { "id": "data:htgfr-11t35", "collection": "data", "collection_id": "htgfr-11t35", "cite_using_url": "https://data.caltech.edu/records/htgfr-11t35", "type": "collection", "title": "Lineage motifs: developmental modules for control of cell type proportions", "author": [ { "family_name": "Tran", "given_name": "Martin", "orcid": "0000-0001-9882-7230" }, { "family_name": "Askary", "given_name": "Amjad" }, { "family_name": "Elowitz", "given_name": "Michael B.", "orcid": "0000-0002-1221-0967" } ], "abstract": "Note that this version is old, new version can be found here: https://doi.org/10.22002/kn8yx-kmb24
In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells that produce specific sets of descendant cell types. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer progenitor states and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell types on lineage trees as potential signatures of committed progenitor states. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in retina and early embryonic development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.
", "doi": "10.22002/htgfr-11t35", "publisher": "CaltechDATA", "publication_date": "2023-05-16" } ]