About

Professor Ernest Fraenkel is the Grover M. Hermann Professor in Health Sciences and Technology at MIT. His laboratory develops computational and experimental approaches to search for new therapeutic strategies for diseases. His research involves new experimental methods that measure cellular changes across the genome and proteome, including genome-wide measurements of transcription, protein-DNA interactions (ChIP-Seq), genetic interactions, and protein modifications. By computationally integrating these diverse data sources, his group reconstructs signaling pathways and uncovers previously unrecognized regulatory mechanisms that contribute to disease etiology, with current projects focusing on cancer, neurodegenerative diseases, and diabetes. Professor Fraenkel received his A.B. in Chemistry and Physics from Harvard College and his Ph.D. in Biology at MIT, working in the laboratory of Professor Carl Pabo. He continued post-doctoral research as a fellow at Harvard University in Professor Stephen Harrison's laboratory. His professional background includes roles as a Whitehead Fellow and Pfizer Computational Biology Fellow at the Whitehead Institute, and he joined MIT as a Research Affiliate at the MIT Computer Science and Artificial Intelligence Laboratory before becoming an Assistant Professor in the Department of Biological Engineering in 2006.

Research topics

• Biology
• Genetics
• Computer Science
• Computational biology
• Cell biology
• Neuroscience
• Medicine
• Bioinformatics
• Immunology
• Pathology
• Internal medicine
• Data science
• Cancer research

Selected publications

• IGF1 peptide targets Rett Syndrome astrocytes to degrade IGF binding protein, rescue synaptogenesis and restore mitochondrial function

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-23

  articleOpen access

  Rett syndrome (RTT), a severe neurodevelopmental disorder caused by mutations in MECP2, leads to profound synaptic and circuit deficits in the brain. While neurons have historically been the focus of RTT pathology, emerging evidence implicates astrocytes in non-cell autonomous mechanisms that impair synaptic structure, function and development. Here, we uncover a central role for astrocyte-secreted IGFBP2 in mediating these deficits and demonstrate that treatment with an IGF1-derived peptide restores synapse formation by promoting IGFBP2 degradation. Using an indirect astrocyte-neuron co-culture system, we show that astrocytes derived from RTT model mice suppress excitatory synapse formation in wild-type neurons and that this impairment is reversed when RTT astrocytes are treated with IGF1(1-3) peptide. Proteomic analysis reveals elevated levels of IGFBP2 in RTT astrocytes and their conditioned media. IGF1(1-3) peptide treatment leads to proteasomal degradation of IGFBP2, increasing IGF1 bioavailability, restoring mitochondrial function, and enhancing downstream PI3K/Akt signaling in neurons. Our data define a molecular mechanism by which astrocyte dysfunction in RTT can be rescued and provide a mechanistic basis for the therapeutic efficacy of IGF1(1-3) peptide, including Trofinetide, an FDA-approved IGF1 peptide mimetic, in RTT. Significance Statement: Astrocyte dysfunction is increasingly recognized as a contributor to neurodevelopmental disorders, yet precise mechanisms remain elusive. Here, we identify IGFBP2 as a key astrocyte-derived inhibitor of synaptogenesis in Rett syndrome. We show that an IGF1-derived peptide, IGF1(1-3), depletes IGFBP2 via proteasomal degradation. This restores IGF1 bioavailability and rescues synaptic function in a non-cell-autonomous manner. These findings provide a mechanistic explanation for the clinical efficacy of IGF1 peptide and its mimetics in Rett syndrome, and highlight astrocytes as rational therapeutic targets in neurodevelopmental and other disorders.

  PublisherOA PDFDOI

• Integration of multiomic and multi-phenotypic data identifies biological pathways associated with physical fitness

  Communications Biology · 2026-02-17

  articleOpen access

  Unraveling the complex associations between human phenotypes and molecular pathways can pave the way to improved health and performance, but faces a fundamental challenge: the measurable genes, proteins, and metabolites vastly outnumber the participants in even the largest studies, yielding spurious correlations. To address this, we developed PhenoMol, a bioinformatic framework that integrates comprehensive phenotypic data predictive of outcomes and reduces multi-omic dimensionality using graph theory constrained by prior biological knowledge. This approach generates biologically informed “expression circuits” to identify causal patterns. Applied to a deeply characterized healthy cohort, PhenoMol successfully predicted elite physical performance and outperformed regression models lacking network-based dimensionality reduction. Designed to be versatile and generalizable, PhenoMol enables studies across small and large populations to predict wellness, performance, and disease outcomes. The software is openly available to support future research in health, disease, and performance optimization. PhenoMol integrates graph theory and biological knowledge to reduce multi-omic dimensionality, predict phenotypes, and reveal causal patterns. It outperforms conventional models that lack biological constraints and is openly available for health, performance, and disease research.

  PublisherOA PDFDOI

• Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues

  Genome biology · 2026-03-12 · 1 citations

  articleOpen access

  BACKGROUND: The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. Using the kidney as an emblematic example of a complex organ, we perform a systematic evaluation of multimodal single-cell integration strategies, with heart tissue used for additional methodological validation. RESULTS: We generate a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we develop the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assess integration strategies. "Horizontal" integration of scRNA and snRNA-seq improves cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq has an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration is especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. CONCLUSIONS: Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.

  PublisherOA PDFDOI

• Intersecting impact of CAG repeat and Huntingtin knockout in stem cell-derived cortical neurons

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-25 · 1 citations

  preprintOpen access

  Summary Huntington’s Disease (HD) is caused by a CAG repeat expansion in the gene encoding Huntingtin (HTT ) . While normal HTT function appears impacted by the mutation, the specific pathways unique to CAG repeat expansion versus loss of normal function are unclear. To understand the impact of the CAG repeat expansion, we evaluated biological signatures of HTT knockout ( HTT KO) versus those that occur from the CAG repeat expansion by applying multi-omics, live cell imaging, survival analysis and a novel feature-based pipeline to study cortical neurons (eCNs) derived from an isogenic human embryonic stem cell series (RUES2). HTT KO and the CAG repeat expansion influence developmental trajectories of eCNs, with opposing effects on the growth. Network analyses of differentially expressed genes and proteins associated with enriched epigenetic motifs identified subnetworks common to CAG repeat expansion and HTT KO that include neuronal differentiation, cell cycle regulation, and mechanisms related to transcriptional repression and may represent gain-of-function mechanisms that cannot be explained by HTT loss of function alone. A combination of dominant and loss-of-function mechanisms are likely involved in the aberrant neurodevelopmental and neurodegenerative features of HD that can help inform therapeutic strategies.

  PublisherOA PDFDOI

• Reliable Monitoring of Respiratory Function with Home Spirometry in People Living with Amyotrophic Lateral Sclerosis

  medRxiv · 2025-02-06

  preprintOpen access

  Monitoring respiratory function is essential for assessing the progression of Amyotrophic Lateral Sclerosis (ALS) and planning interventions. Using spirometry data from the Radcliff Study —a fully remote, longitudinal, exploratory study with a cohort of 67 pALS—we demonstrate that flexible coaching, combined with a quality control analysis that excludes values of ‘0’ and timepoints with failed measurement trials, produces consistent remote spirometry results. Our findings indicate that home-measured Slow Vital Capacity (SVC) and Forced Vital Capacity (FVC) evolve similarly and progress linearly over the study period (7.7 ± 4.0 months). This remains true in both slow and fast progressor subpopulations. This observed linearity in respiratory trajectories supports the potential for early, accurate estimation of progression, reinforcing the feasibility of less frequent monitoring without compromising assessment precision, and reducing the burden on both pALS and the healthcare system. Furthermore, our results align with reported in-clinic pulmonary tests, validating remote monitoring as a means to promote more equitable and accessible clinical trial designs.

  PublisherOA PDFDOI

• CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images

  arXiv (Cornell University) · 2025-12-23

  preprintOpen access

  Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellular morphology are typically trained with a single microscopy imaging type. This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels). Here, we present CHAMMI-75, an open access dataset of heterogeneous, multi-channel microscopy images from 75 diverse biological studies. We curated this resource from publicly available sources to investigate cellular morphology models that are channel-adaptive and can process any microscopy image type. Our experiments show that training with CHAMMI-75 can improve performance in multi-channel bioimaging tasks primarily because of its high diversity in microscopy modalities. This work paves the way to create the next generation of cellular morphology models for biological studies.

  PublisherDOI

• Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-06 · 2 citations

  preprint

  The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.

  PublisherDOI

• Intersecting impact of CAG repeat and huntingtin knockout in stem cell-derived cortical neurons

  Neurobiology of Disease · 2025-04-19 · 4 citations

  articleOpen access

  Huntington's Disease (HD) is caused by a CAG repeat expansion in the gene encoding huntingtin (HTT). While normal HTT function appears impacted by the mutation, the specific pathways unique to CAG repeat expansion versus loss of normal function are unclear. To understand the impact of the CAG repeat expansion, we evaluated biological signatures of HTT knockout (HTT KO) versus those that occur from the CAG repeat expansion by applying multi-omics, live cell imaging, survival analysis and a novel feature-based pipeline to study cortical neurons (eCNs) derived from an isogenic human embryonic stem cell series (RUES2). HTT KO and the CAG repeat expansion influence developmental trajectories of eCNs, with opposing effects on growth. Network analyses of differentially expressed genes and proteins associated with enriched epigenetic motifs identified subnetworks common to CAG repeat expansion and HTT KO that include neuronal differentiation, cell cycle regulation, and mechanisms related to transcriptional repression, and may represent gain-of-function mechanisms that cannot be explained by HTT loss of function alone. A combination of dominant and loss-of-function mechanisms are likely involved in the aberrant neurodevelopmental and neurodegenerative features of HD that can help inform therapeutic strategies.

  PublisherDOI

• Integrative multiomic analysis links TDP-43-driven splicing defects to cascading proteomic disruption of ALS/FTD pathways

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-30 · 1 citations

  preprintOpen access

  Loss of nuclear TDP-43 is a hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Although TDP-43 is known to regulate RNA processing, including repression of cryptic exons, we currently lack a systems-level understanding of the consequences of TDP-43 loss. To address this, we generated multiomic datasets, including RNA-seq and proteomics, from human iPSC-derived neurons depleted of TDP-43. We found that differentially spliced genes, many expressing cryptic exons, had the greatest protein reductions. Surprisingly, nearly half of differentially expressed proteins were neither mis-spliced, nor differentially expressed genes; most of these also had no reported mis-splicing in seven additional post-mortem and iPSC-derived neuron datasets. Integrative network analysis identified a high-confidence disease-specific subnetwork of over 700 interacting proteins, enriched for mRNA processing, synaptic function, and autophagy. Comparison with post-mortem ALS and FTD samples revealed convergent protein and pathway disruptions. We experimentally validated network-predicted effects of cryptic splicing in ATG4B, STMN2, and DAPK1. Our analyses reveal new TDP-43-dependent molecular cascades and nominate central genes as potential ALS/FTD therapeutic targets.

  PublisherOA PDFDOI

• An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration

  Nature Communications · 2025-05-20 · 17 citations

  articleOpen accessSenior author

  Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer’s disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer’s disease and identified Alzheimer’s genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer’s disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer’s disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer’s disease. In this study, Leventhal et al. integrate multi-omic data from human and Drosophila models of Alzheimer’s disease to define regulators of age-associated neurodegeneration in Alzheimer’s disease and the pathways through which they act.

  PublisherOA PDFDOI

Recent grants

• Neuron and Glial Cellular Signatures from Normal and Diseased iPS Cells

  NIH · $16.6M · 2014–2021

• Identifying therapeutic pathways targeting medulloblastoma-immune cell interactions

  NIH · $2.7M · 2021–2027

• Embryonal Brain Tumor Networks

  NIH · $3.6M · 2014–2021

• Epigenetic Pathology and Therapy in Huntington's Disease

  NIH · $4.9M · 2015–2025

• Neuron and Glial Cellular Signatures from Normal and Diseased iPS Cells

  NIH · $2.7M · 2014–2020

Frequent coauthors

• Jesús Esteban‐Pérez

  Istituti di Ricovero e Cura a Carattere Scientifico

  92 shared

• James Berry

  Harvard University

  88 shared

• Steven Finkbeiner

  Gladstone Institutes

  88 shared

• Matthieu Moisse

  VIB-KU Leuven Center for Brain & Disease Research

  73 shared

• Nicola Ticozzi

  University of Milan

  72 shared

• Merit Cudkowicz

  57 shared

• Leslie M. Thompson

  University of California, Irvine

  51 shared

• Neil A. Shneider

  Columbia University Irving Medical Center

  47 shared

Education

• Ph.D., Biological Engineering

  Massachusetts Institute of Technology

  1980

• B.S., Chemical Engineering

  University of California, Berkeley

  1975