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Kilian C, Ulrich H, Zouboulis VA, Sprezyna P, Schreiber J, Landsberger T, Büttner M, Biton M, Villablanca EJ, Huber S, Adlung L. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. NPJ Syst Biol Appl 2024; 10:69. [PMID: 38914538 PMCID: PMC11196733 DOI: 10.1038/s41540-024-00395-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 06/14/2024] [Indexed: 06/26/2024] Open
Abstract
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
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Affiliation(s)
- Christoph Kilian
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), D-20246, Hamburg, Germany
| | - Hanna Ulrich
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), D-20246, Hamburg, Germany
| | - Viktor A Zouboulis
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), D-20246, Hamburg, Germany
| | - Paulina Sprezyna
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), D-20246, Hamburg, Germany
| | - Jasmin Schreiber
- Leibniz Institute for the Analysis of Biodiversity Change, D-20146, Hamburg, Germany
| | - Tomer Landsberger
- Department of statistics and data science, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Maren Büttner
- Calico Life Sciences, LLC, South San Francisco, CA, USA
| | - Moshe Biton
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Eduardo J Villablanca
- Division of Immunology and Allergy, Department of Medicine Solna, Karolinska Institutet and University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Samuel Huber
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), D-20246, Hamburg, Germany
| | - Lorenz Adlung
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), D-20246, Hamburg, Germany.
- Hamburg Center for Translational Immunology (HCTI) and Center for Biomedical AI (bAIome), University Medical Center Hamburg-Eppendorf (UKE), D-20246, Hamburg, Germany.
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2
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Ortega OO, Ozen M, Wilson BA, Pino JC, Irvin MW, Ildefonso GV, Garbett SP, Lopez CF. Signal execution modes emerge in biochemical reaction networks calibrated to experimental data. iScience 2024; 27:109989. [PMID: 38846004 PMCID: PMC11154230 DOI: 10.1016/j.isci.2024.109989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/09/2024] Open
Abstract
Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.
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Affiliation(s)
- Oscar O. Ortega
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN 37212, USA
| | - Mustafa Ozen
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
- Multiscale Modeling Group, Comp. Bio. Hub, Altos Laboratories, Redwood City, CA 94065, USA
| | - Blake A. Wilson
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
| | - James C. Pino
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
| | - Michael W. Irvin
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
| | - Geena V. Ildefonso
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN 37212, USA
| | - Shawn P. Garbett
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Carlos F. Lopez
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
- Multiscale Modeling Group, Comp. Bio. Hub, Altos Laboratories, Redwood City, CA 94065, USA
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3
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Zakaria MF, Sonoda S, Kato H, Ma L, Uehara N, Kyumoto-Nakamura Y, Sharifa MM, Yu L, Dai L, Yamauchi-Tomoda E, Aijima R, Yamaza H, Nishimura F, Yamaza T. Erythropoietin receptor signal is crucial for periodontal ligament stem cell-based tissue reconstruction in periodontal disease. Sci Rep 2024; 14:6719. [PMID: 38509204 PMCID: PMC10954634 DOI: 10.1038/s41598-024-57361-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 03/18/2024] [Indexed: 03/22/2024] Open
Abstract
Alveolar bone loss caused by periodontal disease eventually leads to tooth loss. Periodontal ligament stem cells (PDLSCs) are the tissue-specific cells for maintaining and repairing the periodontal ligament, cementum, and alveolar bone. Here, we investigated the role of erythropoietin receptor (EPOR), which regulates the microenvironment-modulating function of mesenchymal stem cells, in PDLSC-based periodontal therapy. We isolated PDLSCs from patients with chronic periodontal disease and healthy donors, referred to as PD-PDLSCs and Cont-PDLSCs, respectively. PD-PDLSCs exhibited reduced potency of periodontal tissue regeneration and lower expression of EPOR compared to Cont-PDLSCs. EPOR-silencing suppressed the potency of Cont-PDLSCs mimicking PD-PDLSCs, whereas EPO-mediated EPOR activation rejuvenated the reduced potency of PD-PDLSCs. Furthermore, we locally transplanted EPOR-silenced and EPOR-activated PDLSCs into the gingiva around the teeth of ligament-induced periodontitis model mice and demonstrated that EPOR in PDLSCs participated in the regeneration of the periodontal ligament, cementum, and alveolar bone in the ligated teeth. The EPOR-mediated paracrine function of PDLSCs maintains periodontal immune suppression and bone metabolic balance via osteoclasts and osteoblasts in the periodontitis model mice. Taken together, these results suggest that EPOR signaling is crucial for PDLSC-based periodontal regeneration and paves the way for the development of novel options for periodontal therapy.
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Affiliation(s)
- Mhd Fouad Zakaria
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
- Department of Periodontology, Kyushu University Graduate School of Dental Science, Fukuoka, Japan
| | - Soichiro Sonoda
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Hiroki Kato
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Lan Ma
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
- Guangdong Provincial Key Laboratory of Stomatology, South China Center of Craniofacial Stem Cell Research, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Norihisa Uehara
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Yukari Kyumoto-Nakamura
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - M Majd Sharifa
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Liting Yu
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Lisha Dai
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Erika Yamauchi-Tomoda
- Department of Oral and Maxillofacial Radiology, Kyushu University Graduate School of Dental Science, Fukuoka, Japan
| | - Reona Aijima
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Saga University, Saga, Japan
| | - Haruyoshi Yamaza
- Department of Pediatric Dentistry, Kyushu University Graduate School of Dental Science, Fukuoka, Japan
| | - Fusanori Nishimura
- Department of Periodontology, Kyushu University Graduate School of Dental Science, Fukuoka, Japan
| | - Takayoshi Yamaza
- Department of Molecular Cell Biology and Oral Anatomy, Kyushu University Graduate School of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
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4
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Yin F, Song T, Wang Z, Liu J, Zhang H, Tang Y, Zhang Z. Hsp70-Bim incoherent feedforward loop contributes to cell-fate heterogeneity and fractional killing. Br J Pharmacol 2024; 181:659-669. [PMID: 37706555 DOI: 10.1111/bph.16245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 04/17/2023] [Accepted: 09/04/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND AND PURPOSE Although chemotherapeutics or molecular targeted drugs often elicit profound initial responses, fractional killing capable of driving acquired resistance can persist. Identifying stress-induced negative feedback or an incoherent feedforward loop (IFFL), which may contribute to fractional killing, is urgently needed. EXPERIMENTAL APPROACH Mathematical modelling was used to identify how and to what extent a recently reported Hsp70-Bim protein-protein interaction (PPI) contributes to the adaptation of the Bcl-2 network. Experimental validation was made by using a specific inhibitor of Hsp70-Bim PPI, S1g-2, as chemical tool. Bifurcation analysis and stochastic simulation were used for the theoretical study of the impact of Hsp70-Bim PPI on cell-fate heterogeneity and factional killing. KEY RESULTS The Hsp70-Bim-AKT circuit forms an IFFL that greatly contributes to the adaptation of the Bcl-2-regulated apoptosis network, thus leading to fractional killing. This adaptive programme enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario. CONCLUSION AND IMPLICATIONS Hsp70-Bim IFFL serves as a molecular pathway induced by DNA damaging drugs or tyrosine kinase inhibitors that enabled fractional killing, whereby acquired resistance emerges. A synergistic strategy is unveiled for overcoming fractional killing by suppressing Hsp70-Bim PPI.
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Affiliation(s)
- Fangkui Yin
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian, China
| | - Ting Song
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian, China
| | - Ziqian Wang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian, China
| | - Jingjing Liu
- School of Life Science and Technology, Dalian University of Technology, Dalian, China
| | - Hong Zhang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian, China
| | - Yao Tang
- School of Life Science and Technology, Dalian University of Technology, Dalian, China
| | - Zhichao Zhang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian, China
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5
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Li F, Lu ZY, Xue YT, Liu Y, Cao J, Sun ZT, Zhang Q, Xu MD, Wang XY, Xu KL, Wu QY. Molecular basis of JAK2 H608Y and H608N mutations in the pathology of acute myeloid leukemia. Int J Biol Macromol 2023; 229:247-259. [PMID: 36529225 DOI: 10.1016/j.ijbiomac.2022.12.121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/16/2022]
Abstract
Risk-stratification of acute myeloid leukemia (AML) based on (cyto)genetic aberrations, including hotspot mutations, deletions and point mutations have evolved substantially in recent years. With the development of next-generation sequence technology, more and more novel mutations in the AML were identified. Thus, to unravel roles and mechanism of novel mutations would improve prognostic and predictive abilities. In this study, two novel germline JAK2 His608Tyr (H608Y) and His608Asn (H608N) mutations were identified and the molecular basis of these mutations in the leukemiagenesis of AML was elucidated. Our results indicated that JAK2 H608Y and H608N mutations disrupted the hydrogen bond between Q656 and H608 which reduced the JH2 domain's activity and abolished interactions between JH1 and JH2 domains, forced JAK2 into the active conformation, facilitated the entrance of substrates and thus caused JAK2 hyperactivation. Further studies suggested that JAK2 H608Y and H608N mutations enhanced the cell proliferation and inhibited the differentiation of Ba/F3 and MV4-11 cells via activating the JAK2-STAT5 signaling pathway. Moreover, rescue experiments demonstrated that mutations repaired the hydrogen bond between Q656 and H608 displayed opposite results. Thus, this study revealed the molecular basis of JAK2 H608Y and H608N mutations in the pathology of AML.
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Affiliation(s)
- Feng Li
- Blood Diseases Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China; Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China; Department of Cell Biology and Neurobiology, Xuzhou Medical University, Xuzhou 221002, China
| | - Zi-Yi Lu
- Blood Diseases Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yu-Tong Xue
- Blood Diseases Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yang Liu
- Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jiang Cao
- Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zeng-Tian Sun
- Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Qi Zhang
- Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Meng-Di Xu
- Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiao-Yun Wang
- College of Life Sciences, Shandong Agricultural University, Shandong 271018, China.
| | - Kai-Lin Xu
- Blood Diseases Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China; Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
| | - Qing-Yun Wu
- Blood Diseases Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China; Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
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6
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Browning AP, Drovandi C, Turner IW, Jenner AL, Simpson MJ. Efficient inference and identifiability analysis for differential equation models with random parameters. PLoS Comput Biol 2022; 18:e1010734. [PMID: 36441811 PMCID: PMC9731444 DOI: 10.1371/journal.pcbi.1010734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/08/2022] [Accepted: 11/14/2022] [Indexed: 11/29/2022] Open
Abstract
Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity through parameters that vary according to probability distributions. As our novel method is based on an approximate likelihood function, it is highly flexible; we demonstrate identifiability analysis using both a frequentist approach based on profile likelihood, and a Bayesian approach based on Markov-chain Monte Carlo. Through three case studies, we demonstrate our method by providing a didactic guide to inference and identifiability analysis of hyperparameters that relate to the statistical moments of model parameters from independent observed data. Our approach has a computational cost comparable to analysis of models that neglect heterogeneity, a significant improvement over many existing alternatives. We demonstrate how analysis of random parameter models can aid better understanding of the sources of heterogeneity from biological data.
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Affiliation(s)
- Alexander P. Browning
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- QUT Centre for Data Science, Queensland University of Technology, Brisbane, Australia
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Christopher Drovandi
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- QUT Centre for Data Science, Queensland University of Technology, Brisbane, Australia
| | - Ian W. Turner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Adrianne L. Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- QUT Centre for Data Science, Queensland University of Technology, Brisbane, Australia
| | - Matthew J. Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- QUT Centre for Data Science, Queensland University of Technology, Brisbane, Australia
- * E-mail:
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7
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Deciphering signal transduction networks in the liver by mechanistic mathematical modelling. Biochem J 2022; 479:1361-1374. [PMID: 35748700 PMCID: PMC9246346 DOI: 10.1042/bcj20210548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/10/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.
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8
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Caulier AL, Sankaran VG. Molecular and cellular mechanisms that regulate human erythropoiesis. Blood 2022; 139:2450-2459. [PMID: 34936695 PMCID: PMC9029096 DOI: 10.1182/blood.2021011044] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/15/2021] [Indexed: 12/03/2022] Open
Abstract
To enable effective oxygen transport, ∼200 billion red blood cells (RBCs) need to be produced every day in the bone marrow through the fine-tuned process of erythropoiesis. Erythropoiesis is regulated at multiple levels to ensure that defective RBC maturation or overproduction can be avoided. Here, we provide an overview of different layers of this control, ranging from cytokine signaling mechanisms that enable extrinsic regulation of RBC production to intrinsic transcriptional pathways necessary for effective erythropoiesis. Recent studies have also elucidated the importance of posttranscriptional regulation and highlighted additional gatekeeping mechanisms necessary for effective erythropoiesis. We additionally discuss the insights gained by studying human genetic variation affecting erythropoiesis and highlight the discovery of BCL11A as a regulator of hemoglobin switching through genetic studies. Finally, we provide an outlook of how our ability to measure multiple facets of this process at single-cell resolution, while accounting for the impact of human variation, will continue to refine our knowledge of erythropoiesis and how this process is perturbed in disease. As we learn more about this intricate and important process, additional opportunities to modulate erythropoiesis for therapeutic purposes will undoubtedly emerge.
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Affiliation(s)
- Alexis L Caulier
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA; and
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA; and
- Broad Institute of MIT and Harvard, Cambridge, MA
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