Driving Biological Projects


Current COBA Driving Biological Projects are listed below. Interested in becoming one of our Driving Biological Project partners? See our page on how to our collaborate with us or fill out our Project Inquiry form. 


Principal Investigator


Project Description

Robert Edwards
University of California - San Francisco

The Edwards lab studies the biochemistry and mechanisms of neurotransmission; this involves timing how long it takes for vesicles (such as those found at synapses) to release their cargo. We have worked with the lab to help identify and quantify excyotosis events in high speed time lapse movies. While previously identification of the events was manual, we have used Trackmate (link) alongside a Jupyter notebook to automatically detect the events and integrate these detections with the lab's own code for event classification. See our current workflow here.

Jonathan Sexton
University of Michigan
The Sexton lab uses explanted pancreatic islets to study metabolic disorders and diabetes. These explants are difficult to image and analyze because they are 3D structures and because some cell types within the explants are rare but still need to be identified and quantified accurately. We worked with the lab to develop a pipeline in CellProfiler that utilizes a Cellpose deep learning model to segment nuclei and find cells in explants. See our current workflow here. To aid in classifying cells from these images into types, we've also added the ability to work with 3D datasets to CellProfiler Analyst.
Charles Ndawula Jr
National Livestock Resources Research Institute,  Uganda
Dr Ndawula studies tick-borne pathogens in livestock and develops methods to control their negative impact. Different species of ticks pose varying threats to their host, so determining the species of tick is essential. However, identification of tick species can be nuanced, often subjective and relies on visual guides as found here. The aim of this DBP is to overcome the burden of understanding subtle details of ticks for new researchers by creating an intuitive deep learning based tool for the classification of tick species for use while out in the field.
Jeff Hardin
University of Wisconsin
The Hardin Lab studies epidermal cell rearrangement in the C. elegans embryo using four-dimensional (4D) Nomarski microscopy. Previously, the lab relied on manual tracing of cells in these movies, making analysis and tracking slow. We’ve worked with the lab to create a FIJI script to find in-focus plane on 4D datasets, and a Cellpose2 model to segment the epidermal cells. We're now working to create an automatic scoring of intercalation defects using TrackMate and improving the model to detect all cells in the embryo.
Amy Barczak
Massachusetts General Hospital
The Barczak lab studies how tuberculosis damages lung tissue to cause fibrosis and disease using colormetric staining techniques on mouse lung tissue sections. Quantifying fibrosis is difficult because the staining techniques used generate color images which can vary by staining batch and by animal. We will work with the lab to generate a workflow using machine learning in ilastik and CellProfiler to identify and quantify blue regions of fibrosis within images of lung tissue sections.
Phil Newmark
Morgridge Institute for Research
The Newmark Lab uses planarians to study regeneration and germ development. We have worked with the lab to create workflows to align serial sections for 3D histopathology, challenging due to planarians' small size.  We have also created a number of workflows for 3D fluorescence imaging of testes, ovaries, and yolk glands. Initial fluorescence workflows made in CellProfiler have been improved to take advantage of Cellpose models.
Melissa Skala
University of Wisconsin
The Skala lab uses photonics-based technologies such as FLIM to develop personalized treatment plans for cancer patients (including breast, pancreatic, colorectal, neuroendocrine, oral, and other cancers). Because of the image modality, the number of cell types and the different cell culture conditions (2D and 3D), automated segmentation has been historically challenging. We have used CellPose 2.0 and CellProfiler to train deep learning models to segment the nuclei/cell and cytoplasm in a collection of cell types and then perform measurements on the segmented objects.
GIF of fluorescence image and label matrix
Thomas Gaborski
Rochester Institute of Technology
The Gaborski lab studies cell-substrate interactions, specifically how cells plated on different substrates secrete ECM molecules like collagen to generate fibers visualized with fluorescence microscopy. These images are challenging to analyze because the fibers form irregular, nonconvex shapes and are very heterogeneous in brightness. We've worked with the lab to generate a workflow using machine learning in ilastik and CellProfiler to segment fibers in these images. This single model works well across a variety of different fiber types and imaging setups. We've written up a blogpost detailing this project here.
Bill Bement
University of Wisconsin
The Bement Lab studies cytoskeletal organization and signaling. In order to help them detect particular patterns in microscopy images, we have developed a number of CellProfiler plugins, including plugins for scrambling pixels to destroy pre-existing structure and calculating the optimal variance sizes and variance metrics within an image.
Amy Shaub Maddox
University of North Carolina - Chapel Hill
The Maddox lab examines the intricate cellular structures involved in cell division using a variety of model systems. One such system is C. elegans, which the Maddox lab uses to study oogenesis with a desire to understand various characteristics of oocytes, such as their position and shape in 3D. These 3D images are difficult to segment since there is no nuclear stain and each cell is only defined by an intense staining at the cell borders. Previous methods used by the lab relied entirely on manual analysis which is time consuming and low-throughput. This DBP aims to solve this by having and automated workflow for the segmentation and analysis of cell shape, size and position. Materials for CellProfiler and deep-learning approaches are available here.
Chris Doe
University of Oregon
The Doe Lab studies Drosophila central nervous system development, and to need to trace neurites in order to calculate metrics like branching and volume; currently this is only possible to the degree of accuracy needed with manual tracing. We are working to automate the segmentation process using deep learning techniques.
Mary Harrington
Smith College
The Harrington Lab uses bioluminescent markers to study circadian rhythms in suprachiasmatic nuclei (SCN). These intravital images are challenging to segment because of the difficulty to align cells/regions between time-points since the marker/signal can remain off for hours. We are working to align the images through the entire data set prior to the segmentation.