{"doi":"10.1101/2020.07.24.219659","title":"A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids","abstract":"1 Abstract Cells react to their microenvironment by integrating external stimuli into phenotypic decisions via an intracellular signaling network. Even cells with deregulated signaling can adapt to their environment. To analyze the interplay of environment, neighborhood, and cell state on phenotypic variability, we developed an experimental approach that enables multiplexed mass cytometric imaging to analyze up to 240 pooled spheroid microtissues. This system allowed us to quantify the contributions of environment, neighborhood, and intracellular state to phenotypic variability in spheroid cells. A linear model explained on average more than half of the variability of 34 markers across four cell lines and six growth conditions. We found that the contributions of cell-intrinsic and environmental factors are hierarchically interdependent. By overexpression of 51 signaling protein constructs in subsets of cells, we identified proteins that have cell-intrinsic and extrinsic effects, exemplifying how cell states depend on the cellular neighborhood in spheroid culture. Our study deconvolves factors influencing cellular phenotype in a 3D tissue and provides a scalable experimental system, analytical principles, and rich multiplexed imaging datasets for future studies.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":125214,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9427,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":502278,"name":"Matthias Leutenegger","orcid":null,"position":1,"is_corresponding":false},{"id":434367,"name":"Xiao‐Kang Lun","orcid":"0000-0003-0534-469X","position":2,"is_corresponding":false},{"id":267863,"name":"Fanny Georgi","orcid":"0000-0002-4803-5099","position":3,"is_corresponding":false},{"id":501534,"name":"Natalie de Souza","orcid":"0000-0003-4286-8951","position":4,"is_corresponding":false},{"id":44509,"name":"Bernd Bodenmiller","orcid":"0000-0002-6325-7861","position":5,"is_corresponding":false},{"id":502277,"name":"Vito RT Zanotelli","orcid":null,"position":0,"is_corresponding":true}],"reference_count":64,"raw_metadata":null,"created_at":"2026-07-18T23:15:15.482227Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}