Biology subjects

Alizada, S.

Publications and source records attributed to Alizada, S..

3 recordsLinked to original sources

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↗

bioRxiv2025

QUILPEN provides independent and label-free single-cell quantification of pigmentation dynamics and organelle content

The relationship between melanogenesis, pigmentation, and melanocyte behavior is complex. In melanocytes, pigmentation is often associated with differentiation, yet mature melanocytes vary in pigment content. In melanoma, pigmentation-linked transcriptional programs may have prognostic value, but visual assessments of tumor pigmentation have yielded inconsistent results. Progress linking pigmentation phenotypes to cell state has been limited by a lack of tools that can directly and dynamically quantify melanin content in live cells. Here we present QUantitative Imaging of Label-free Pigment-associated ENtities (QUILPEN), a label-free multi-modal imaging technique that combines quantitative phase imaging (QPI), quadrant darkfield (QDF), and absorption imaging, to independently capture light that has been transmitted, scattered, and absorbed. This non-destructive method enables live-cell imaging over multiple days without labels. We show absorption as a reliable readout of melanin content, which can be decoupled from melanosome content detected by QDF, which measures scattered light. Applying QUILPEN to melanoma cells before and during repigmentation, we find that melanin content is highly heterogeneous, and that this heterogeneity is reinstated upon repigmentation. Lineage tracking further reveals that melanin synthesis rates are heritable and can be transmitted both symmetrically and asymmetrically. QUILPEN enables real-time quantification of pigmentation dynamics and cell-level heterogeneity. SignificancePigmentation is a defining feature of melanocytic cells and linked to behavior, yet melanins optical properties complicate direct measurement with existing approaches. Taking advantage of the absorbing nature of pigment and scattering properties of organelles, we can independently track pigment and organelle content over time and along cell lineages in a label-free manner to reveal population heterogeneity and patterns of inheritance. Research HighlightsQUILPEN is a label-free, live cell imaging pipeline that independently quantifies pigment and organelles in melanocytic cells. Tracking pigment and organelle content along cell lineages reveals varied inheritance of pigment-associated features. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/683473v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@8d1ef9org.highwire.dtl.DTLVardef@ec6ebdorg.highwire.dtl.DTLVardef@5a7b64org.highwire.dtl.DTLVardef@5b0d80_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

Receptor tyrosine kinase inhibition leads to regression of acral melanoma by targeting the tumor microenvironment

Acral melanoma (AM) is an aggressive melanoma variant that arises from palmar, plantar, and nail unit melanocytes. Compared to non-acral cutaneous melanoma (CM), AM is biologically distinct, has an equal incidence across genetic ancestries, typically presents in advanced stage disease, is less responsive to therapy, and has an overall worse prognosis. Independent analysis of published genomic and transcriptomic sequencing identified that receptor tyrosine kinase (RTK) ligands and adapter proteins are frequently amplified, translocated, and/or overexpressed in AM. To target these unique genetic changes, a zebrafish acral melanoma model was exposed to a panel of narrow and broad spectrum multi-RTK inhibitors, revealing that dual FGFR/VEGFR inhibitors decrease acral-analogous melanocyte proliferation and migration. The potent pan-FGFR/VEGFR inhibitor, Lenvatinib, uniformly induces tumor regression in AM patient-derived xenograft (PDX) tumors but only slows tumor growth in CM models. Unlike other multi-RTK inhibitors, Lenvatinib is not directly cytotoxic to dissociated AM PDX tumor cells and instead disrupts tumor architecture and vascular networks. Considering the great difficulty in establishing AM cell culture lines, these findings suggest that AM may be more sensitive to microenvironment perturbations than CM. In conclusion, dual FGFR/VEGFR inhibition may be a viable therapeutic strategy that targets the unique biology of AM.

cancer biology↗