Company initiates Scientific Summit and unveils library of new support resources for researchers to incorporate multidimensional morphology assessments into grant applications for increased funding
MENLO PARK, Calif. — November 1, 2022 — Deepcell, a life science company pioneering AI-powered single-cell morphology characterization technology for biology and translational research, today announced the roll out of the next stage of its Technology Access Program (TAP), providing access to the novel capabilities of the Deepcell platform. The University of California, San Francisco (UCSF) and the Translational Genomics Research Institute (TGen), part of City of Hope, are the initial locations to have the first generation of the Deepcell platform installed through the TAP program, which is expanding to 3 more sites across the U.S. and Europe.
“We have taken a significant step forward within our Technology Access Program to put the power of our Deepcell platform into the hands of end-users,” said Marc Montserrat, Chief Business Officer of Deepcell. “Having world-renowned researchers applying Deepcell technology to investigate morphological differences in a wide range of applications using cell lines as well as primary body fluid and solid tissue samples will accelerate the commercial rollout of our AI-powered single-cell image analysis and isolation platform for cell biology. The possibilities for how the Deepcell platform can be used to generate novel insights are extremely broad.”
“We are pleased to be one of the first institutions to access the Deepcell platform in our own lab,” said Dr. Hani Goodarzi, Associate Professor Department of Biochemistry & Biophysics at UCSF. “Using Deepcell technology, we aim to study time-course morphological changes in drugged lymphoblast cells. This would allow us to understand drug response and efficacy in ways that was not possible before. I am excited by the initial results and we already have many other research ideas that can be enabled with this technology.”
“We are excited to be a part of the technology access program to test Deepcell’s platform. Our goal is to characterize tumor cells found in malignant effusions from patients with metastatic breast cancer. Using this technology, we can perform label-free enrichment and isolation of rare tumor cells from these fluids. Initial results from copy number profiling of enriched tumor cell fractions show genomic alterations consistent with those frequently found in breast cancer. Next, we plan to test the feasibility of single-cell RNA sequencing of tumor cells captured using Deepcell’s platform to further elucidate the pathobiology of the disease. Evaluating the unique tumor microenvironment in malignant effusions provides an opportunity to look at tumor biology in a unique space that may behave differently than those in solid tumors, which ultimately may help to identify novel therapeutic targets,” said Dr. Mark Magbanua, Laboratory Medicine at UCSF
Deepcell is pioneering an innovative area of single-cell biology analysis focused on multidimensional readouts of cell morphology at scale without the use of any cellular markers. The company has developed AI-based technology that iteratively learns to identify and capture single cells based on morphological features that are barely discernible to the human eye. Morphologically similar clusters of cells can then be captured for downstream molecular analysis without affecting viability or perturbing them. By unlocking the power of morphology for cell biology, Deepcell is advancing the use of deep learning capabilities to better understand cellular heterogeneity in richer detail.
With a multi-phased approach, Deepcell is driving toward the commercialization of its AI-powered platform. The company is well-positioned to set a new standard for the industry with AI-centric single cell morphology analysis that is relevant for applications in characterization of complex samples, cell atlasing, cell and gene therapy, functional screening, cancer biology, and stem cell research.
“It’s exciting to begin operating the instrument in-house at TGen as a part of the Technology Access Program. Our goal is to explore the use of this instrument for translational research in determining how melanoma tumor cells respond to different treatments,” said Candice Wike, Ph.D., Manager of TGen’s Scientific Technology Assessment Research Team.
Expanding the Adoption of Quantitative Single-Cell Morphology
In addition, Deepcell held its inaugural Scientific Summit earlier this month in San Jose, California with a select group of experts and innovators in pathology, imaging cytometry, computational biology, and molecular biology. At the event, scientific leaders from across the U.S., Europe, and Asia heard about the latest data from Deepcell, gave their feedback on technology roadmaps, and worked together to formulate a clear set of data creation studies that will help to advance the adoption of high-content morphology across life sciences and biotech.
“There is no question that cellular “form” is linked to cellular function. The team at Deepcell have developed technology that can select cells in flow based on morphometric characteristics that has both discovery science and clinical research applications,” said Dr. Andrew Filby, IMA Theme Lead and FCCF Director at Newcastle University. “ It means we can “close the loop” with regard to genome, transcriptome and proteome in terms of the morpholome.”
Deepcell also announced that it would support researchers’ efforts to incorporate multidimensional morphology assessment into their grant applications. The company has developed a series of written resources for describing the technology that researchers can tap into for writing assistance. Scientists interested in hearing more about the Deepcell technology or incorporating the Deepcell technology into their grants can reach out to Deepcell directly at: email@example.com.
Deepcell is advancing the understanding of cell biology by combining advances in AI and high throughput, quantitative cell classification and capture of single cells to deliver novel insights through an unprecedented view of cell biology. Founded in 2017, the company has created unique, microfluidics-based technology that uses continuously learning AI to classify cells based on detailed visual features without labeling and capture them without inherent bias, just based on their morphological profiles. The Deepcell platform maintains cell viability for downstream molecular analysis. Deepcell is privately held and based in Menlo Park, CA. For more information, please visit www.deepcell.com.