Biology subjects

Marks, K. A.

Publications and source records attributed to Marks, K. A..

2 recordsLinked to original sources

bioRxiv2026

Evolution of immune infiltration and immune triad formation across early melanoma progression

Tumor-infiltrating lymphocytes (TIL) in melanoma are a clinically important prognostic marker and are routinely used to inform patient care. However, when immune recognition of transformed melanocytes begins and how it evolves during early melanoma progression is not well understood. Using spatial transcriptomics and a unique cohort of patient-matched lesions representing multiple stages of early evolution, we investigate T cells, dendritic cells (DCs) and multi-cell immune "triads" during melanoma initiation. We find that T cell and DC density, triad density, and triad proximity to melanoma cells are elevated in regions of Stage I invasive melanoma as compared to Stage 0 in situ lesions and nevi. Further, we find that triad abundance correlates with both T cell and DC density, but is not predicted by clustering patterns of these cell types over larger spatial scales. We conclude that immune recognition of melanoma and engagement with the lesion significantly increases with progression from Stage 0 to Stage I disease. Further, using nevi that are depth-matched in the dermis to Stage I invasive lesions, we demonstrate this increased immune recognition is not a consequence of dermal invasion alone. This points to a paradigm where immune engagement of invasive melanoma reflects recognition of melanoma cell-intrinsic changes that emerge in Stage I disease.

cancer biology↗

bioRxiv2026

Temporal dynamics improves machine learning-based prediction of cell state from quantitative phase imaging

Cell morphology reflects cell health and can distinguish cell-cycle stage, growth arrest, and distinct pathways of cell death. Live, label-free quantitative phase imaging (QPI) captures these features non-invasively and with high temporal resolution, yet many image-based classifiers rely on single frames and cannot separate states whose differences emerge only over time. How much temporal information is needed, and which architecture best exploits it, remain open questions. We assembled 1,874 QPI timelapse sequences spanning six cell states (interphase, mitosis, cell cycle arrest, apoptosis, ferroptosis, and necroptosis) and compared two-dimensional convolutional neural networks (CNNs) with a three-dimensional (3D) spatiotemporal CNN across increasing frame counts. Accuracy improved as frames were added, with the largest gain between one and three frames. The 2D models saturated beyond three frames, whereas the 3D architecture kept improving, reaching 96.5% accuracy and a 3.5% error rate at eleven frames. The temporal information needed tracked the timescale of each process: mitosis was resolved from a single frame, while ferroptosis benefited most from extended sequences. Overall, these results show that dynamic information, rather than static morphology alone, drives accurate cell-state classification, and that 3D architectures are needed to fully exploit it for label-free dynamic phenotyping.

bioengineering↗