Biology subjects

Moen, E.

Publications and source records attributed to Moen, E..

5 recordsLinked to original sources

bioRxiv2024

Cellular behavior analysis from live-cell imaging of TCR T cell-cancer cell interactions

T cell therapies, such as chimeric antigen receptor (CAR) T cells and T cell receptor (TCR) T cells, are a growing class of anti-cancer treatments. However, expansion to novel indications and beyond last-line treatment requires engineering cells dynamic population behaviors. Here we develop the tools for cellular behavior analysis of T cells from live-cell imaging, a common and inexpensive experimental setup used to evaluate engineered T cells. We first develop a state-of-the-art segmentation and tracking pipeline, Caliban, based on human-in-the-loop deep learning. We then build the Occident pipeline to collect a catalog of phenotypes that characterize cell populations, morphology, movement, and interactions in co-cultures of modified T cells and antigen-presenting tumor cells. We use Caliban and Occident to interrogate how interactions between T cells and cancer cells differ when beneficial knock-outs of RASA2 and CUL5 are introduced into TCR T cells. We apply spatiotemporal models to quantify T cell recruitment and proliferation after interactions with cancer cells. We discover that, compared to a safe harbor knockout control, RASA2 knockout T cells have longer interaction times with cancer cells leading to greater T cell activation and killing efficacy, while CUL5 knockout T cells have increased proliferation rates leading to greater numbers of T cells for hunting. Together, segmentation and tracking from Caliban and phenotype quantification from Occident enable cellular behavior analysis to better engineer T cell therapies for improved cancer treatment.

cell biology↗

bioRxiv2021

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

bioinformatics↗

bioRxiv2019

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

While live-cell imaging is a powerful approach to studying the dynamics of cellular systems, converting these imaging data into quantitative, single-cell records of cellular behavior has been a longstanding challenge. Deep learning methods have proven capable of performing cell segmentation--a critical task for analyzing live-cell imaging data--but their performance in cell tracking has been limited by a lack of dynamic datasets with temporally consistent single-cell labels. We bridge this gap through the integrated development of labeling and deep learning methodology. We present a new framework for scalable, human-in-the-loop labeling of live-cell imaging movies, which we use to label a large collection of movies of fluorescently labeled cell nuclei. We use these data to create a new deep-learning-based cell-tracking method that achieves state-of-the-art performance in cell tracking. We have made all of the data, code, and software publicly available with permissive open-source licensing through the DeepCell projects web portal https://deepcell.org.

systems biology↗

bioRxiv2018

DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the analysis of biological images but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~$250, with a sub-$100 cost achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org.

systems biology↗

bioRxiv2018

Automatic interpretation of otoliths using deep learning

The age structure of a fish population has important implications for recruitment processes and population fluctuations, and is key input to fisheries assessment models. The current method relies on manually reading age from otoliths, and the process is labor intensive and dependent on specialist expertise.\n\nAdvances in machine learning have recently brought forth methods that have been remarkably successful in a variety of settings, with potential to automate analysis that previously required manual curation. Machine learning models have previously been successfully applied to object recognition and similar image analysis tasks. Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images.\n\nWe adapt a standard neural network model designed for object recognition to the task of estimating age from otolith images. The model is trained and validated on a large collection of images of Greenland halibut otoliths. We show that the model works well, and that its precision is comparable to and may even surpass that of human experts.\n\nAutomating this analysis will help to improve consistency, lower cost, and increase scale of age prediction. Similar approaches can likely be used for otoliths from other species as well as for reading fish scales. The method is therefore an important step forward for improving the age structure estimates of fish populations.

ecology↗