{"id":"W3196194138","doi":"10.1038/s41467-021-25279-y","title":"Live cell tagging tracking and isolation for spatial transcriptomics using photoactivatable cell dyes","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Human Genome Research Institute; National Cancer Institute; Ragon Institute of MGH, MIT and Harvard; National Institute of General Medical Sciences; Hertz Foundation; Searle Scholars Program; National Science Foundation; Damon Runyon Cancer Research Foundation; National Institutes of Health; U.S. Department of Health and Human Services; Howard Hughes Medical Institute","keywords":"Phenotype; Transcriptome; Cell; Cell biology; Biology; Function (biology); Computational biology; Gene; Genetics; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000338638,0.0003333388,0.0003389883,0.000341406,0.0003367212,0.0006953554,0.000576648,0.0005503102,0.001522911],"category_scores_gemma":[0.0004936747,0.0002709802,0.0004295766,0.000430712,0.0005758003,0.0004197908,0.0007207762,0.0007277591,0.0005017287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006080565,"about_ca_system_score_gemma":0.0006849043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001441338,"about_ca_topic_score_gemma":0.004595855,"domain_scores_codex":[0.9997938,0.00002101289,0.00001028695,0.00007809134,0.00007492022,0.00002183095],"domain_scores_gemma":[0.9997359,0.00009359129,0.0000540593,0.00006119765,0.00003440264,0.0000207901],"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":[0.00004988557,0.00002129943,0.0009253256,0.00009169114,0.0000144084,0.00005060793,0.00004949934,0.009388667,0.9678432,0.006971671,0.0004629896,0.01413079],"study_design_scores_gemma":[0.00001596166,0.0001021291,0.002977464,0.00001902234,0.00002076985,0.0001656736,0.00006693666,0.1568232,0.8227763,0.004671531,0.01231776,0.00004315298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1308769,0.0003116869,0.8631453,0.0001434195,0.0000641232,0.00009208826,0.001083508,0.001514014,0.002769007],"genre_scores_gemma":[0.5476227,0.0007281837,0.4446201,0.0001917995,0.0000247373,0.0005136969,0.001616991,0.0004397849,0.004241943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001522911,"threshold_uncertainty_score":0.005094588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02617038921833141,"score_gpt":0.2765614337336516,"score_spread":0.2503910445153201,"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."}}