Automated sectioning, imaging, and extraction of renal glomeruli for single-cell analysis Understanding heterogeneous cell states is essential for studying and treating diverse diseases of the kidney. Renal glomeruli, 100-200 ?m across, are abundant but sparse, and implicated in many causes of chronic kidney disease. The proposed method will automate the sectioning, imaging, and extraction of renal glomeruli with single-cell resolution. This approach will sample large populations of glomeruli from human tissue and register serial sections into digital 3d reconstructions. Using this technique, this project aims to ?quantify single cells in whole glomeruli?, ?identify a panel of compatible immunostaining markers, and assess the feasibility of capturing serial frozen tissue sections. ?Together, these will validate new methods to study cell distribution and protein expression in glomerular disease. More broadly, this will facilitate additional automation of sectioning, staining, imaging, and analysis for single-cell experiments. This will enable quantitative, single-cell research in renal pathology, benefiting scientific discovery as well as the development of new treatments.

Public Health Relevance

Automated sectioning, imaging, and extraction of renal glomeruli for single-cell analysis One in seven Americans suffers from chronic kidney disease (CKD), a leading cause of death and morbidity with limited options for early detection. This project will develop a new approach to study the three-dimensional distribution of single cells in renal glomeruli, microscopic structures that are currently difficult to study. This will help researchers better understand causes of CKD, potentially leading to new treatments and earlier intervention.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43DK120281-01
Application #
9680322
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Gossett, Daniel Robert
Project Start
2018-09-15
Project End
2019-08-31
Budget Start
2018-09-15
Budget End
2019-08-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
3scan, Inc.
Department
Type
DUNS #
968428446
City
San Francisco
State
CA
Country
United States
Zip Code
94110