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Opportunity at National Institute of Standards and Technology (NIST)

Predicting Cell Health and Function for Cell Therapy and Regenerative Medicine

Location

Material Measurement Laboratory, Biosystems and Biomaterials Division

RO# Location
50.64.41.C0185 Gaithersburg, MD

Please note: This Agency only participates in the February and August reviews.

Advisers

Name E-mail Phone
Sarkar, Sumona sumona.sarkar@nist.gov 301-975-8595

Description

Cell count and cell viability are two of the most fundamental cellular measurements in biology.  These measurements are conducted across the biotechnology industry for research, product development, and product manufacturing of biologics and cell therapies and are used for patient dosing of novel CAR-T therapies.  The most common approach to evaluate cell count and viability is based on automated microscopy analysis of suspended cell samples.  Automated image-based cell analyzers can provide rapid in-process and in-line assessment of cell manufacturing.  It is becoming apparent however, that the information provided from simplified binary distinction of live and dead cells does not capture the continuum of cell health and function inherent in a population of cells and is insufficient for informing the cell manufacturing process or product safety and effectiveness.  To fit the purpose of in-process and in-line measurements, imaging data sets are often limited to those of low resolution and with limited features (cells appear as rounded objects) however, these data sets may contain sufficient biophysical information that if properly mined and analyzed, can help to identify cell health and cell function which are critical attributes in characterizing cell-based products.  The goal of this project is to elevate the commonly conducted cell count and viability measurement to meet current industry needs for a fast, reliable, user-friendly, multiplexed measurement to monitor the quality of cell-based products and manufacturing processes.  Identifying relationships between these image data sets and the health and function of cell populations will require a combination of measurement assurance for image data/data analysis quality and deep learning-based strategies with well-designed orthogonal cell assays to provide appropriate training data sets to predict cell health and function.

Keywords:
Machine Learning; Cell Therapy; Cell Viability; Biomanufacturing; Regenerative Medicine; Bioassay; Imaging; Live Cell Microscopy; Potency Assays; Functional Assays

Eligibility

Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral applicants
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