Publications

Highlights

(For a full list see below or go to Google Scholar)

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

Single cell analysis of live-cell imaging experiments requires identifying cells in single frames and linking them together over time. In this work, we solve the segmentation and linkage problems by creating a large annotated dataset for live-cell imaging. We use this data to train deep learning models that achieve state-of-the-art performance in cell tracking and lineage construction.

E Moen, E Borba, G Miller, M Schwartz, D Bannon, N Koe, I Camplisson, D Kyme, C Pavelchek, T Price, T Kudo, E Pao, W Graf, and D Van Valen

bioRxiv 10.1101/803205v2

Dynamic allocation of computational resources for deep learning-enabled cellular image analysis with Kubernetes

Deep learning has the potential to transform single cell image analysis, but delivering models to end users is challenging because of deep learning’s hardware and software requirements. We partnered with Cloud Posse to create a turn-key Kubernetes deployment of DeepCell in the cloud. Using this approach we can analyze millions of images on the timescale of hours.

D Bannon, E Moen, M Schwartz, E Borba, S Cui, K Huang, I Camplisson, N Koe, D Kyme, B Chang, E Pao, E Osterman, W Graf, and D Van Valen

bioRxiv 10.1101/505032v3

Deep learning for cellular image analysis

Deep learning is transforming our ability to analyze the behavior of cells in microscope images. In this review, we survey the field’s progress on adapting these methods to perform classification, segmentation, object tracking, and latent information extraction on biological images. We point readers to existing software packages for each application, as well as to annotated datasets.

E Moen, D Bannon, T Kudo, W Graf, M Covert, and D Van Valen

Nature Methods (1), 1-14 (2019)

A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging

Spatial genomics technologies let us measure “omics” type data in a fashion that preserves spatial information, but the data analysis is challenging. We partnered with the Angelo lab to apply DeepCell to spatial proteomics data of triple-negative breast cancer to quantify the interactions between immune cells and tumor cells in the tumor microenvironment.

L Keren, M Bosse, D Marquez, R Angoshtari, S Jain, S Varma, S Yang, A Kurian, D Van Valen, R West, S Bendall, and M Angelo

Cell 174 (6), 1373-1387. e19 (2018).

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation

Understanding how signaling dynamics are decoded into patterns of gene expression is challenging because imaging and genomics technologies don’t talk to each other. In this work, we performed live-cell imaging and single-cell RNA sequencing in the same individual cell to understand how NF-κB dynamics are turned into distinct patterns of gene expression.

K Lane*, D Van Valen*, M DeFelice, D Macklin, T Kudo, A Jaimovich, A Carr, T Meyer, D Pe’er, S Boutet, and M Covert

Cell Systems 4 (4), 458-469. e5 (2017).

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Using computer vision to obtain single-cell resolution is often the most challenging aspect of live-cell imaging experiments. In this work, we used deep learning to perform single-cell image segmentation of microscopy images across the domains of life.

D Van Valen, T Kudo, K Lane, D Macklin, N Quach, M DeFelice, I Maayan, Yu Tanouchi, E Ashley, and M Covert

PLOS Computational Biology 12 (11), 1-24 (2016).

This work was highlighted in press releases by the Hertz Foundation and CrowdFlower

A Single-Molecule Hershey Chase Experiment

How bacteriophage lambda delivers its genome into bacterial cells is an open problem. To test different proposed biophysical mechanisms, we measured the DNA transfer between individual viruses and bacteria.

D Van Valen*, D Wu*, Y Chen, H Tuson, P Wiggins, and R Phillips

Current Biology 22 (14), 1339-1343 (2012).

This work was featured in Science magazine’s editors choice section and in a press release by Caltech.

 

Full List

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning
E Moen, E Borba, G Miller, M Schwartz, D Bannon, N Koe, I Camplisson, D Kyme, C Pavelchek, T Price, T Kudo, E Pao, W Graf, and D Van Valen
bioRxiv 10.1101/803205v2

Dynamic allocation of computational resources for deep learning-enabled cellular image analysis with Kubernetes
D Bannon, E Moen, M Schwartz, E Borba, S Cui, K Huang, I Camplisson, N Koe, D Kyme, B Chang, E Pao, E Osterman, W Graf, and D Van Valen
bioRxiv 10.1101/505032v3

Deep learning for cellular image analysis
E Moen, D Bannon, T Kudo, W Graf, M Covert, and D Van Valen
Nature Methods (1), 1-14 (2019)

A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging
L Keren, M Bosse, D Marquez, R Angoshtari, S Jain, S Varma, S Yang, A Kurian, D Van Valen, R West, S Bendall, and M Angelo
Cell 174 (6), 1373-1387. e19 (2018).

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation
K Lane*, D Van Valen*, M DeFelice, D Macklin, T Kudo, A Jaimovich, A Carr, T Meyer, D Pe’er, S Boutet, and M Covert
Cell Systems 4 (4), 458-469. e5 (2017).

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
D Van Valen, T Kudo, K Lane, D Macklin, N Quach, M DeFelice, I Maayan, Yu Tanouchi, E Ashley, and M Covert
PLOS Computational Biology 12 (11), 1-24 (2016).

A Single-Molecule Hershey Chase Experiment
D Van Valen*, D Wu*, Y Chen, H Tuson, P Wiggins, and R Phillips
Current Biology 22 (14), 1339-1343 (2012).

Ion-dependent dynamics of DNA ejections for bacteriophage λ
D Wu*, D Van Valen*, Q Hu, and R Phillips
Biophysical journal 99 (4), 1101-1109 (2010).

Biochemistry on a leash: the roles of tether length and geometry in signal integration proteins
D Van Valen, M Haataja, and R Phillips
Biophysical journal 96 (4), 1275-1292.

*: Both authors contributed equally.