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

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org

Principles of open source bioinstrumentation applied to the poseidon syringe pump system

The poseidon syringe pump and microscope system is an open source alternative to commercial systems. It costs less than $400 and can be assembled in under an hour using the instructions and source files available at https://pachterlab.github.io/poseidon. We describe the poseidon system and use it to illustrate design principles that can facilitate the adoption and development of open source bioinstruments. The principles are functionality, robustness, safety, simplicity, modularity, benchmarking, and documentation

A model for regulation by SynGAP-α1 of binding of synaptic proteins to PDZ-domain 'Slots' in the postsynaptic density

SynGAP is a Ras/Rap GTPase-activating protein (GAP) that is a major constituent of postsynaptic densities (PSDs) from mammalian forebrain. Its α1 isoform binds to all three PDZ (PSD-95, Discs-large, ZO-1) domains of PSD-95, the principal PSD scaffold, and can occupy as many as 15% of these PDZ domains. We present evidence that synGAP-α1 regulates the composition of the PSD by restricting binding to the PDZ domains of PSD-95. We show that phosphorylation by Ca^(2+)/calmodulin-dependent protein kinase II (CaMKII) and Polo-like kinase-2 (PLK2) decreases its affinity for the PDZ domains by several fold, which would free PDZ domains for occupancy by other proteins. Finally, we show that three critical postsynaptic signaling proteins that bind to the PDZ domains of PSD-95 are present in higher concentration in PSDs isolated from mice with a heterozygous deletion of synGAP

DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes

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 ~US$250, with a cost below US$100 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/

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

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

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

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org

Binding of synGAP to PDZ Domains of PSD-95 is Regulated by Phosphorylation and Shapes the Composition of the Postsynaptic Density

SynGAP is a Ras/Rap GTPase-activating protein (GAP) present in high concentration in postsynaptic densities (PSDs) from mammalian forebrain where it binds to all three PDZ (PSD-95, Discs-large, ZO-1) domains of PSD-95. We show that phosphorylation of synGAP by Ca^(2+)/calmodulin-dependent protein kinase II (CaMKII) decreases its affinity for the PDZ domains as much as 10-fold, measured by surface plasmon resonance. SynGAP is abundant enough in postsynaptic densities (PSDs) to occupy about one third of the PDZ domains of PSD-95. Therefore, we hypothesize that phosphorylation by CaMKII reduces synGAP′s ability to restrict binding of other proteins to the PDZ domains of PSD-95. We support this hypothesis by showing that three critical postsynaptic signaling proteins that bind to the PDZ domains of PSD-95 are present at a higher ratio to PSD-95 in PSDs isolated from synGAP heterozygous mice

A model for regulation by SynGAP-α1 of binding of synaptic proteins to PDZ-domain 'Slots' in the postsynaptic density

SynGAP is a Ras/Rap GTPase-activating protein (GAP) that is a major constituent of postsynaptic densities (PSDs) from mammalian forebrain. Its α1 isoform binds to all three PDZ (PSD-95, Discs-large, ZO-1) domains of PSD-95, the principal PSD scaffold, and can occupy as many as 15% of these PDZ domains. We present evidence that synGAP-α1 regulates the composition of the PSD by restricting binding to the PDZ domains of PSD-95. We show that phosphorylation by Ca^(2+)/calmodulin-dependent protein kinase II (CaMKII) and Polo-like kinase-2 (PLK2) decreases its affinity for the PDZ domains by several fold, which would free PDZ domains for occupancy by other proteins. Finally, we show that three critical postsynaptic signaling proteins that bind to the PDZ domains of PSD-95 are present in higher concentration in PSDs isolated from mice with a heterozygous deletion of synGAP

DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes

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 ~US$250, with a cost below US$100 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/