Anne Carpenter on Muck Rack

Anne Carpenter

Cambridge
As seen in: Nature, EurekAlert!, The New England Journal of Medicine, The Scientist, Quanta Magazine, European Journal of Marketing, Reno Gazette-Journal, Mondaq, Soap Central, F1000Research
Discovery in images. Enthralled by science. SAB Recursion, Quiver. Building SyzOnc. Opinions my own. bsky.app/profile/drannecarpenter.bsky.social
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Articles

nELISA: a high-throughput, high-plex platform enables quantitative profiling of the inflammatory secretome - Nature Methods

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By Milad Dagher, Andy Ng, Philippe DeCorwin-Martin, Shafqat Rasool, Jiamin Huang, Srinivas Niranj Chandrasekaran, Adam Skepner, Shantanu Singh, Anne Carpenter, Jeffrey D. Munzar, Woojong Rho
| Nature Verified
Abstract Existing high-plex protein measurement tools compromise on quantification, precision and cost efficiency. Here, to address this, we present nELISA, a platform that combines a DNA-mediated, bead-based sandwich immunoassay with advanced multicolor bead barcoding. Antibody pairs are preassembled on target-specific, barcoded beads, which ensures spatial separation between noncognate assays.
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Combining phenomics with transcriptomics reveals cell-type-specific morphological and molecular signatures of the 22q11.2 deletion - Nature Communications

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By Matthew Tegtmeyer, Yu Han, Kathryn Hebert, Gregory P. Way, Beth Cimini, Anne Carpenter, Shantanu Singh
| Nature Verified
Abstract Neuropsychiatric disorders remain difficult to treat due to complex and poorly understood mechanisms. NeuroPainting is a high-content morphological profiling assay based on Cell Painting and optimized for human stem cell–derived neural cell types, including neurons, progenitors, and astrocytes. The assay quantifies over 4000 features of cell structure and organelle organization, generating a dataset suitable for phenotypic screening in neural models.
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A versatile information retrieval framework for evaluating profile strength and similarity - Nature Communications

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By Alexandr A. Kalinin, John Arevalo, Loan Vulliard, Suganya Sivagurunathan, Anne Carpenter, Gregory P. Way
| Nature Verified
Abstract Large-scale profiling assays capture a cell population’s state by measuring thousands of biological properties per cell or sample. However, evaluating profile strength and similarity remains challenging due to the high dimensionality and non-linear, heterogeneous nature of measurements. Here, we develop a statistical framework using mean average precision (mAP) as a single, data-driven metric to address this challenge.
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