A Statistically Rigorous Multi-Scale Texture Analysis Framework for 3D Spheroid Characterization: Temporal Autocorrelation Correction and Molecular Validation

A Statistically Rigorous Multi-Scale Texture Analysis Framework for 3D Spheroid Characterization: Temporal Autocorrelation Correction and Molecular Validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Statistically Rigorous Multi-Scale Texture Analysis Framework for 3D Spheroid Characterization: Temporal Autocorrelation Correction and Molecular Validation Daniel G. Regassa, Marat S. Babaev, Evgeniya Y. Shabalina, Philipp Y. Maximov, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8508394/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Three-dimensional tumor spheroids represent dynamic biomodeling systems exhibiting emergent collective behaviors—spontaneous reorganization, migration dynamics, and epithelial-mesenchymal transition observable through label-free time-lapse microscopy. However, optical opacity from light scattering limits fluorescence imaging depth, preventing single-cell resolution deep within spheroid volumes and necessitating ensemble-level morphological analysis rather than exhaustive single-cell profiling, while destructive molecular endpoint assays provide only discrete temporal snapshots, missing continuous dynamic changes defining biological processes. We present a validated computational framework for statistically rigorous ensemble morphological profiling, integrating multi-scale texture analysis (gray-level co-occurrence matrices, wavelet decomposition, Gabor filtering; 37 features spanning orientations and scales), global standardization eliminating scale artifacts while preserving biological signal, and autocorrelation-informed block bootstrap resampling addressing temporal dependence. Proof-of-concept analysis using representative A549 and H1299 lung cancer spheroids (n = 1 per condition, 48 hourly observations) demonstrates three framework capabilities. First, global standardization normalized features spanning 24 orders of magnitude in variance while preserving biological discrimination (16.8-fold Fisher score difference between cell lines). Second, temporal autocorrelation analysis revealed 4-hour median decorrelation lags, addressed through 5-hour block bootstrap resampling enabling valid statistical inference. Third, external validation using Cancer Dependency Map RNA-sequencing (1,699 cell lines) demonstrated that texture discrimination (16.8-fold) corresponded quantitatively to independent molecular differences (14.6-fold VIM/CDH1 ratio), achieving concordance within 15%. This framework enables statistically valid, biologically grounded morphological profiling for continuous monitoring applications including drug screening, organoid development tracking, and biomanufacturing quality control where ensemble dynamics complement discrete molecular measurements. Biological sciences/Biological techniques Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics 3D spheroid imaging texture analysis temporal autocorrelation block bootstrap epithelial-mesenchymal transition Full Text Additional Declarations No competing interests reported. Supplementary Files DataS1DataDictionary.csv TableS1ExtendedEMTPanel.xlsx TableS2FeatureDefinitions.xlsx TableS3CompleteStatisticalResults.xlsx DataS1CompleteFeatureMatrix.csv FigureS1MathematicalValidation.png SUPPLEMENTARYMETHODSS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers invited by journal 28 Jan, 2026 Editor assigned by journal 28 Jan, 2026 Editor invited by journal 19 Jan, 2026 Submission checks completed at journal 15 Jan, 2026 First submitted to journal 15 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8508394","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":582080628,"identity":"182e49ff-51a8-4a5e-b5f3-c3311234ec31","order_by":0,"name":"Daniel G. 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