Bukavina, L. | Search bioRxiv

bioRxiv2026

InVitroGap: an open-source tool for automated quantification of wound closure in the in vitro scratch assay

BackgroundScratch assays are widely used to study wound closure in vitro, but quantitative image analysis remains constrained by manual variability, proprietary workflows, and tools requiring programming expertise. We developed InVitroGap, a Python-based application with a browser-accessible interface for automated quantification of scratch assay closure from sequential microscopy images. MethodsRCC-ER and Renca cells were seeded in 96-well ImageLock plates and scratched using a WoundMaker device for uniform linear wounds or a 200 {micro}L pipette tip for crisscross wounds. Phase-contrast time-lapse images acquired at 0, 24, and 48 h with an IncuCyte SX5 system were independently analyzed using IncuCyte 2023A Rev2 and InVitroGap. The InVitroGap pipeline combines Gaussian smoothing, gradient-based texture mapping, adaptive percentile thresholding, and morphological post-processing to quantify wound confluence and relative wound density (RWD). Agreement was evaluated using paired comparisons, Pearson and Spearman correlations, Bland-Altman analysis, and mean absolute error (MAE). ResultsInVitroGap measurements closely tracked IncuCyte outputs across both cell lines, with no significant between-method differences (p > 0.05), strong pooled correlations (R{superscript 2} = 0.964 for RWD; R{superscript 2} = 0.983 for wound confluence), and small mean biases (absolute bias [≤] 1.64%). The tool successfully processed crisscross wounds from brightfield image series, and a complete four-timepoint series was analyzed in approximately 10 seconds, with robust performance across distinct cell morphologies and wound geometries. ConclusionsInVitroGap provides a transparent, computationally efficient, and platform-independent alternative for scratch assay analysis, delivering performance comparable to commercial systems while remaining freely accessible at https://invitrogap.vercel.app/. HighlightsO_LIOpen-source Python tool for automated, platform-independent in vitro scratch assay analysis C_LIO_LITexture-based adaptive pipelines enable robust wound segmentation across cell types C_LIO_LIQuantifies wound confluence and relative wound density from time-lapse images C_LIO_LIStrong agreement with IncuCyte measurements in the tested datasets C_LI

bioinformatics↗

bioRxiv2023

Role of Gut Microbiome in Neoadjuvant Chemotherapy Response in Urothelial Carcinoma: A Multi-Institutional Prospective Cohort Evaluation

Treatment with neoadjuvant chemotherapy (NAC) in muscle invasive bladder cancer (MIBC) is associated with clinical benefit in urothelial carcinoma. While extensive research evaluating role of tumor mutational expression profiles and clinicopathologic factors into chemoresponse has been published, the role of gut microbiome (GM) in bladder cancer in chemoresponse has not been thoroughly evaluated. A working knowledge of the microbiome and its effect on all forms of cancer therapy in BC is critical. Here we examine gut microbiome of bladder cancer patients undergoing NAC. Overall, there was no significant difference in alpha and beta diversity by responder status. However, analysis of fecal microbiome samples showed that a higher abundance of Bacteroides within both institutional cohorts during NAC was associated with residual disease at the time of radical cystectomy regardless of chemotherapy regimen. Group community analysis revealed presence of favorable microbial subtypes in complete responders. Finally, fecal microbial composition outperformed clinical variables in prediction of complete response (AUC 0.88 vs AUC 0.50), however, no single microbial species could be regarded as a fully consistent biomarker. Microbiome-based community signature as compared to single microbial species is more likely to be associated as the link between bacterial composition and NAC response.

cancer biology↗