Biology subjects

Lippincott, M. J.

Publications and source records attributed to Lippincott, M. J..

3 recordsLinked to original sources

bioRxiv2025

High-content live-cell time-lapse imaging predicts cells about to die via apoptosis

Cell death is a dynamic process that unfolds through time. Live-cell time-lapse imaging captures these dynamics in a way thats impossible for static snapshots. High-content imaging (HCI), which has been developed for static microscopy, applied to time-lapse imaging can quantify how single-cell states change through time. Here we show the ability of high-content live-cell time-lapse imaging (HCLTI) to quantify the onset and progression of one form of cell death called apoptosis. We apply the Live Cell Painting assay called ChromaLIVETM and develop an HCLTI analysis pipeline. We show that HCLTI can discern the morphology dynamics of cells undergoing apoptosis, and demonstrate that machine learning can predict apoptosis as early as 100 minutes after exposing HeLa cells to the apoptosis inducer Staurosporine. This technical advancement paves the way for future studies to better understand the dynamics of other forms of cell death. Understanding cell death dynamics is one piece of solving larger biomedical puzzles like understanding how cells resist death (e.g., therapeutic resistance of cancer cells) and how cells die too soon (e.g., neurodegeneration).

systems biology↗

bioRxiv2025

Scalable data harmonization for single-cell image-based profiling with CytoTable

High-content imaging (HCI) involves the automated acquisition and quantitative analysis of cell phenotypes from microscopy images. These studies often rely on screening, which can involve thousands of chemical or genetic perturbations that produce terabytes of microscopy data. To extract meaningful biological insights, this data must be processed into quantitative features through a technique known as image-based profiling. A major analytical bottleneck is curating the high-dimensional, single-cell data derived from varied image analysis tools. These datasets suffer from inconsistent schemas, inefficient file formats, and undocumented ontological relationships. These challenges reduce reproducibility and slow progress in downstream applications. To solve these issues, we introduce CytoTable1, a software package for harmonizing single-cell image-based profiling. CytoTable enables modular, portable, and cross-language data integration through a robust, reproducible, and scalable engine that harmonizes single-cell readouts from multiple image analysis tools, preparing for feature integration with software in the Cytomining ecosystem such as Pycytominer.2

bioinformatics↗

bioRxiv2024

A morphology and secretome map of pyroptosis

Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1{beta} processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and control cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.

systems biology↗