{"id":"W4200189327","doi":"10.1038/s43018-021-00301-w","title":"Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment","year":2021,"lang":"en","type":"article","venue":"Nature Cancer","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":217,"is_retracted":false,"has_abstract":true,"ca_institutions":"Provincial Health Services Authority; University of British Columbia","funders":"Universitätsspital Zürich; Universität Zürich; European Commission; National Cancer Institute; National Institutes of Health; National Science Foundation; Cancer Research UK; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Mass cytometry; Context (archaeology); Computational biology; Cell biology; Tumor microenvironment; Function (biology); Biology; Molecular imaging; Flow cytometry; Human breast; Breast cancer; Cancer; Pathology; Tumor cells; Cancer research; Immunology; Phenotype; Medicine; Gene","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":[0.0007687316,0.0005634277,0.0004693095,0.00103007,0.0003211455,0.001053297,0.0005103591,0.0008303678,0.001636738],"category_scores_gemma":[0.000590207,0.0004078732,0.0003793719,0.0005256718,0.0006059665,0.0004765295,0.0006901287,0.001118939,0.0007338718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007182683,"about_ca_system_score_gemma":0.0004359347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005398168,"about_ca_topic_score_gemma":0.001323605,"domain_scores_codex":[0.9995907,0.00009301928,0.00001960483,0.00009147546,0.0001704104,0.00003473948],"domain_scores_gemma":[0.9996634,0.0001385805,0.00004901572,0.00005389164,0.00005769682,0.00003757198],"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":[0.00002765111,0.00002070947,0.0002842176,0.00005956263,0.00001016662,0.00002751264,0.00002462281,0.0007162298,0.9824556,0.003159408,0.0004968509,0.01271744],"study_design_scores_gemma":[0.00001041349,0.0000492945,0.0009402485,0.00001742073,0.00001587274,0.000231855,0.00001990064,0.02762104,0.9570864,0.001783988,0.01218811,0.00003547834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0713721,0.003234008,0.917272,0.0007068544,0.0001932205,0.000233366,0.001008409,0.001787373,0.00419271],"genre_scores_gemma":[0.258064,0.00273349,0.7344681,0.0005548802,0.00008752904,0.0006450565,0.0007714244,0.000131582,0.002543819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001636738,"threshold_uncertainty_score":0.005475461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005853556192739166,"score_gpt":0.2216356467453715,"score_spread":0.2157820905526323,"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."}}