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Going beyond segmentation in discovering early pixel level changes in diabetic nephropathy using histology image and urinary proteomics data
Summary Data Summary
Applicant Sarder, Pinaki
E-Mail Address Pinaki.Sarder@medicine.ufl.edu
Project Title Going beyond segmentation in discovering early pixel level changes in diabetic
nephropathy using histology image and urinary proteomics data
CBU ID 21AU4180
External SubContract ID 32307-95
Diabetic Complication Nephropathy
Funding Program Group Pilot & Feasibility [PF2021]
Abstract In the USA, approximately 34.2 million people have diabetes. Twenty-five percent
of this population will develop diabetic nephropathy (DN). Diabetes is the most
common cause of end stage renal disease (ESRD), accounting for approximately 45%
of cases. In DN, manual scoring of renal microstructural damage often does not
correlate with disease progression. Moreover, while glucose control is known to
be the most acceptable treatment for diabetes, intensive control of glucose may
not reduce the risk of DN. This gap in the literature prompts investigation of
two overarching and interlinked questions in diabetes: (i) what are the early
digital biomarkers that can be measured from histologically stained renal
biopsies which predict DN and (ii) can we identify links between early image
biomarkers and molecular pathways in DN. Answering these questions will help to
better stratify DN patients into different risk categories, and aid precise
strategic targeting of new and hopefully better treatment strategies for
preventing or treating DN. The biggest investigative challenge is that there
exist no open-source databases of well-curated digital images of DN renal
biopsies with matching high throughput molecular data. While the generation of
spatial transcriptomics or single cell/ nuclei RNA sequencing (scRNAseq /
snRNAseq) data matched with renal biopsy of early DN cases is becoming
technically possible, the sample sizes available from earlier efforts are still
too low to answer the proposed questions. In this study, we will collaborate
with nephropathologists, nephrologists, and molecular biologists to answer the
proposed questions using matching renal tissue image and urinary proteomic (as
molecular) data collected from patients with early and late DN stages. Our
primary aim is to identify biological correlations between renal microstructural
image patterns and urinary proteins in DN patients. Over the last six years, the
PI has generated numerous results discussing objective quantification of renal
micro-compartments, computational classification of renal diseases, and
computational prediction of clinical biometrics from renal tissue images. In
this study, the PI is refocusing his team’s efforts to investigate the molecular
biomarkers from urinary proteomics and correlating/integrating the resulting
information with matched histomorphometrics. This newly developed framework will
answer the outlined questions pertaining to DN diagnosis and prognostication.
The developed framework can be extended to conduct similar studies using other
molecular omics data such as scRNAseq, snRNAseq, spatial transcriptomics,
spatial metabolomics, and laser dissected spatial seq data.
Application PDF Application Research Plan
Status Contract Executed
Key Personnel Sarder, Pinaki (eRA Commons User Name: PSARDER1)
Tomaszewski, John (eRA Commons User Name: TOMASZEWSKI)
Rosenberg, Avi (eRA Commons User Name: AVIROSENBERG)
Jain, Sanjay (eRA Commons User Name: JAINSA)
Salary Total Costs 46434
Supply Total Costs 0
Equipment Total Costs 0
Travel/Other Total Costs 18616
Direct Costs 65050
Indirect Costs Proposed 34950
Total Costs Proposed 100000
Total Costs Approved 60000
Start Date 7/1/2021
End Date 6/30/2022
IFO Name Mical, Alison
IFO E-Mail Address alodojew@buffalo.edu
IACUC/IRB No. 99999
IACUC/IRB Institution SUNY at Buffalo
Entity ID No. 14-1368361
Report Request Date 6/30/2022
T1D NO
TypeCount
Invoices 9
Progress Reports 1
Data Submission


Invoices
UrlCBU IDExternal IDInstitutionDateDirectIndirectInvoiceBalancePDF
  View  21AU418032307-95SUNY at Buffalo9/1/2022$531.62$316.32$847.94$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo7/17/2022$4,308.21$2,563.38$6,871.59$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo6/10/2022$9,566.73$2,329.26$11,895.99$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo5/6/2022$3,882.37$2,310.01$6,192.38$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo4/8/2022$6,382.07$3,797.33$10,179.40$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo3/17/2022$3,840.95$2,285.37$6,126.32$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo2/14/2022$3,872.85$2,304.34$6,177.19$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo12/8/2021$3,539.97$2,106.28$5,646.25$1,322.29View PDF
  View  21AU418032307-95SUNY at Buffalo1/11/2022$2,972.19$1,768.46$4,740.65$1,322.29View PDF


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