{"id":"W3024554698","doi":"10.1016/j.cell.2020.04.048","title":"An Engineered CRISPR-Cas9 Mouse Line for Simultaneous Readout of Lineage Histories and Gene Expression Profiles in Single Cells","year":2020,"lang":"en","type":"article","venue":"Cell","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":342,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Institute of Diabetes and Digestive and Kidney Diseases; European Molecular Biology Organization; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; Leukemia and Lymphoma Society; Howard Hughes Medical Institute","keywords":"Biology; CRISPR; Computational biology; Lineage (genetic); Stem cell; Transcriptome; Genetics; Cas9; Genome editing; Gene; Single-cell analysis; Cell; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005978012,0.000608585,0.0005974057,0.0008840128,0.0004303111,0.0007551361,0.001159288,0.0009260072,0.002647667],"category_scores_gemma":[0.0003713508,0.0006630088,0.0005108811,0.0004926923,0.0004848856,0.0004611184,0.0006267417,0.001769343,0.001754978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005747544,"about_ca_system_score_gemma":0.0005700018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001106978,"about_ca_topic_score_gemma":0.003624281,"domain_scores_codex":[0.99939,0.00003589342,0.00007931954,0.0002142927,0.0002158871,0.00006459256],"domain_scores_gemma":[0.9995085,0.00008988667,0.0001386973,0.000107174,0.00006672634,0.00008906698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002498806,0.00001156749,0.00007273144,0.00001599452,0.000004092164,0.00003367823,0.00001111745,0.00005672342,0.9981167,0.0003507986,0.0001176733,0.001183944],"study_design_scores_gemma":[0.00001485152,0.00003604284,0.001018268,0.000006596867,0.00001628894,0.0002747769,0.000008735463,0.00188876,0.989061,0.0001376079,0.007523158,0.00001397853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4175951,0.001227151,0.5438661,0.0007878359,0.0005666186,0.0008561303,0.01677942,0.009592122,0.008729468],"genre_scores_gemma":[0.5890942,0.001227171,0.3593937,0.0005846978,0.00007176781,0.001254123,0.01124033,0.002295859,0.03483807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002647667,"threshold_uncertainty_score":0.00885731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705444061470135,"score_gpt":0.2225586759846533,"score_spread":0.205504235369952,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}