{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T09:23:33Z","timestamp":1774430613074,"version":"3.50.1"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016394","name":"Ludwig Center at Harvard","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016394","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Understanding how cells respond to internal and external inputs requires investigating cells within three-dimensional (3D) environments, which better mimic physiological conditions. Compared to two-dimensional (2D) systems, 3D cultures more accurately simulate tissue architecture, including cell-cell and cell-extracellular matrix interactions, as well as gradients of oxygen and nutrients. Despite these advantages, quantifying signaling dynamics in 3D remains difficult due to limitations in imaging depth, phototoxicity, and computational analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed experimental and computational tools for tracking individual cells\u2019 responses in 3D. We focused on the response of human breast cancer cells to irradiation using a cell line that expresses a fluorescent reporter for the cell cycle regulator p21, which is activated by the tumor suppressor p53 after irradiation. We embedded individual cells and multicellular spheroids in a dual-Matrigel assay and used light sheet fluorescence microscopy (LSFM) to obtain high-resolution images at several time points post-irradiation. We then developed computational pipelines to obtain detailed reconstructions and quantitative analyses of p21 dynamics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Individual dispersed cells exhibited a gradual, monotonic increase in the fraction of p21-positive cells, with the majority of cells becoming positive 24 h after irradiation. When applied to spheroids, the same system captured a transient decrease in the fraction of p21-positive cells post-irradiation, followed by a delayed pronounced rise only at 24 h. In addition, while the fraction of p21-positive cells increased in both systems, p21 intensity within induced cells remained relatively constant. This behavior is consistent with studies in 2D cultures showing that irradiation induces p53 oscillations, with each p53 pulse regulating the probability, rather than the magnitude, of p21 transcription. Notably, spatial mapping of annotated nuclei showed no dependence between p21 levels and radial cell position within spheroids. Comparisons between 2D, 3D single-cell, and spheroid data indicate that while the overall extent of p21 activation is similar across systems, the kinetics differ, with spheroids exhibiting slower induction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The differences in features such as p21 induction kinetics observed in spheroids compared to 2D and 3D single-cell cultures post-irradiation likely reflect p21 signaling specific to cells in 3D configurations with cell\u2013extracellular matrix constraints. Overall, the platform developed in this study provides a powerful framework to dissect heterogeneous signaling dynamics in physiologically relevant 3D contexts and can be extended to assess the effects of drug treatments on other complex multicellular structures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbioe.2026.1735308","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:52:11Z","timestamp":1774421531000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantifying single-cell responses to irradiation in 3D"],"prefix":"10.3389","volume":"14","author":[{"given":"Joshua","family":"Fran\u00e7ois","sequence":"first","affiliation":[{"name":"Department of Systems Biology, Blavantik Institute, Harvard Medical School","place":["Boston, MA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alina","family":"Simerzin","sequence":"additional","affiliation":[{"name":"Department of Systems Biology, Blavantik Institute, Harvard Medical School","place":["Boston, MA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashwini","family":"Jambhekar","sequence":"additional","affiliation":[{"name":"Department of Systems Biology, Blavantik Institute, Harvard Medical School","place":["Boston, MA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Galit","family":"Lahav","sequence":"additional","affiliation":[{"name":"Department of Systems Biology, Blavantik Institute, Harvard Medical School","place":["Boston, MA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1038\/nrc2657","article-title":"p21 in cancer: intricate networks and multiple activities","volume":"9","author":"Abbas","year":"2009","journal-title":"Nat. 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