Our Role in Kidney Precision Medicine Project (KPMP)

Kidney Precision Medicine Project (KPMP) is a multi-year collaboration of leading research institutions to study patients with kidney disease. Goal is to better understand the mechanisms of acute kidney injury (AKI) and chronic kidney disease (CKD) using renal tissue biopsies collected from participants diagnosed with AKI and CKD, deep interrogation of the tissues using various cutting-edge molecular omics technologies, fusion of the tissue image and molecular omics data using computational methods, and disbursing the raw data, tools, resources via KPMP atlas for the larger scientific communities. KPMP consists of 34 funded components at over 40 research institutions across the U.S.

KPMP operates via unique collaboration between National Institutes of Health, KPMP recruitment sites (RSs), Tissue Interrogation Sites (TISs), KPMP Central Hub, Kidney Tissue Atlas Coordinating Center, a.k.a. Kidney Mapping Atlas Project (KMAP), and KPMP Opportunity Pool Awardees. Detailed roles and description of individual teams are available here . Dr. Sarder is part of the KMAP investigator team (MPIs: Dr. Mathias Kretzler and Dr. Jonathan Himmelfarb) and Co-Leads the activities of Digital Pathology Working Group (KMAP DPWG) with Dr. Laura Barisoni. Brief details of KMAP DPWG activities is provided below.

KMAP Digital Pathology Working Group

The Digital Pathology Working Group (KMAP DPWG) is made up of researchers who specialize in computational image analysis, bioinformatics, and clinical pathology. The group focuses on analyzing digital pathology and spatial omics data from diagnostic and research cores that are available through the KPMP. These data will be shared on the KPMP Atlas with the help of the KPMP—KMAP Visualization Group. It is expected that these data will be used to answer various scientific questions in downstream studies. The KMAP DPWG also works with various TISs to ensure that appropriate quality control/quality assurance measures are in place for data generated by TISs and that appropriate pre- and post-analytical metadata are included with TIS-generated data.

Our envisioned pipeline for open access computational pathology and data sharing
Digital pathology brightfield histology and spatial omics data are analyzed using various already implemented and optimized computational pipelines by various computational groups for image data QC/QA, detection and segmentation of structures, and alignment with molecular data. The derived data/ knowledge is expected to be shared via KPMP atlas for answering various biological questions.

KMAP DPWG Team Members

  • Laura Barisoni – Co-Lead (Computational Pathology, Professor, Duke University)
  • Pinaki Sarder – Co-Lead (Computational Microscopy, Associate Professor, University of Florida)
  • Charles E. Alpers (Renal Pathology, Professor, University of Washington)
  • Ulysses Balis (Pathology Informatics, Professor, University of Michigan)
  • Jeffery Hodgin (Renal Pathology, Associate Professor, University of Michigan)
  • Andrew R. Janowczyk (Digital Pathology, Assistant Professor, Case Western Reserve University)

Contributing Workforce

  • Nicholas Lucarelli (University of Florida)
  • Samuel Border (University of Florida)
  • Yijiang Chen (Case Western Reserve University)
  • Bangchen Wang (Duke University)

Contributions of CMIL to KPMP

M. Keller, N. Lucarelli, Y. Chen, B. Wang, C. E. Alpers, A. Janowczyk, J. B. Hodgin, S. P. Border, S. Mimar, A. Naglah§, N. Bonevich, U. G. Balis, J. Himmelfarb, M. Kretzler, L. Barisoni, N. Gehlenborg, and P. Sarder, for the Kidney Precision Medicine Project, “Interactive visualization of kidney structural segmentations and associated pathomic features on whole slide images,” ASN Kidney Week 2023, Philadelphia, PA, Nov. 1-5, 2023.

X. Chen, R. Seaflon, M. Weiguang, P. Zhicheng, B. Lake, M. Rajasree, M. Ferreira, A. Naglah§, P. M. Palevsky, J. Torrealba, C. R. Parikh, S. Rosas, K. Kiryluk, J. A. Schaub, L. Barisoni, P. Sarder, J. B. Hodgin, M. T. Eadon, S. Jain, M. Kretzler, and O. Troyanskaya, for the Kidney Precision Medicine Project, “A network-based view of AKI and CKD at cell subtype and spatial niche resolution,” ASN Kidney Week 2023, Philadelphia, PA, Nov. 1-5, 2023.

B. Lutnick, D. Manthey, J. U. Becker, B. Ginley, K. Moos, J. E. Zuckerman, L. Rodrigues, A.J. Gallan, L. Barisoni, C. E. Alpers, X. X. Wang, K. Myakala, B. A. Jones, M. Levi, J. B. Kopp, T. Yoshida, S. S. Han, S. Jain, A. Z. Rosenberg, K. Y. Jen, and P. Sarder, for the Kidney Precision Medicine Project, “A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology,” Communications Medicine (London), vol. 2, pp. 105: 1-15, Aug. 2022.

P. Sarder, B. Ginley§ , N. Lucarelli , Y. Chen, J. B. Hodgin, A. Rosenberg, C. Alpers, A. Madabhushi, L. Barisoni, and U Balis, for the Kidney Precision Medicine Project, “A computational pipeline for segmentation and classification of tubules,” ASN Kidney Week 2022, Orlando, FL, Nov. 1-6, 2022.

N. Lucarelli, B. Ginley§, R. M. Ferreira, U. Balis, S. Jain, J. Tomaszewski, T. E. Achkar, M. T. Eadon, and P. Sarder, for the Kidney Precision Medicine Project, “Computational segmentation of glomeruli to align histomorphology with spatial transcriptomic signature,” ASN Kidney Week 2022, Orlando, FL, Nov. 1-6, 2022.

B. Lutnick, A. Z. Rosenberg, L. Barisoni, C. E. Alpers, Y. Chen, A. Janowczyk, A. Madabhushi, J. Torrealba, A. Weins, I. E. Stillman, L. C. Herlitz, L. Rodrigues, J. E. Zuckerman, S. Jain, U. G. Balis, K. Y. Jen, and P. Sarder, for the Kidney Precision Medicine Project, “Computational quantification of IFTA for CKD cases of Kidney Precision Medicine Project,” ASN Kidney Week 2021, Online (due to COVID-19 pandemic), Nov. 2-7, 2021. [Selected for a platform presentation by Mr. B. Lutnick.]

P. Sarder, Y. Chen, B. Ginley, A. Z. Rosenberg, A. Janowczyk, B. Lutnick, N. Lucarelli, C. E. Alpers, S. Jain, S. Grewenow, B. Steck, L. Barisoni, A. Madabhushi, and U. G. Balis, for the Kidney Precision Medicine Project, “Prognostic glomerular morphometric phenotype discovery via clustering across large datasets,” ASN Kidney Week 2021, Online (due to COVID-19 pandemic), Nov. 2-7, 2021.

Underline indicates corresponding author.
* indicates equal contribution.
indicates Dr. Sarder’s faculty trainees.
§ indicates post doctoral associates.
indicates Dr. Sarder’s graduate students.
indicates Dr. Sarder’s undergraduate students.