Jarod Rutledge is currently a joint postdoctoral fellow at EMBL Heidelberg and Stanford University. His research is focused on the application of deep learning tools to cellular imaging and omics data to enable new experimental paradigms in functional genomics and translational medicine. Jarod received his Ph.D. from Stanford University School of Medicine, where he worked with Professor Tony Wyss-Coray and Professor Stephen Montgomery. He researched ways to combine proteomics, genetics, and machine learning to discover new quantitative biomarkers of aging, Alzheimer's disease, and Parkinson's disease to enable precision preventative medicine. Jarod has also made previous contributions to the fields of medicinal chemistry and synthetic biology, where he worked to develop new therapies for neglected tropical diseases and inflammatory bowel diseases.

All Publications

  • Post-translational modifications linked to preclinical Alzheimer's disease-related pathological and cognitive changes. Alzheimer's & dementia : the journal of the Alzheimer's Association Abiose, O., Rutledge, J., Moran-Losada, P., Belloy, M. E., Wilson, E. N., He, Z., Trelle, A. N., Channappa, D., Romero, A., Park, J., Yutsis, M. V., Sha, S. J., Andreasson, K. I., Poston, K. L., Henderson, V. W., Wagner, A. D., Wyss-Coray, T., Mormino, E. C. 2023


    In this study, we leverage proteomic techniques to identify communities of proteins underlying Alzheimer's disease (AD) risk among clinically unimpaired (CU) older adults.We constructed a protein co-expression network using 3869 cerebrospinal fluid (CSF) proteins quantified by SomaLogic, Inc., in a cohort of participants along the AD clinical spectrum. We then replicated this network in an independent cohort of CU older adults and related these modules to clinically-relevant outcomes.We discovered modules enriched for phosphorylation and ubiquitination that were associated with abnormal amyloid status, as well as p-tau181 (M4: β = 2.44, p < 0.001, M7: β = 2.57, p < 0.001) and executive function performance (M4: β = -2.00, p = 0.005, M7: β = -2.39, p < 0.001).In leveraging CSF proteomic data from individuals spanning the clinical spectrum of AD, we highlight the importance of post-translational modifications for early cognitive and pathological changes.

    View details for DOI 10.1002/alz.13576

    View details for PubMedID 38146099

  • Organ aging signatures in the plasma proteome track health and disease. Nature Oh, H. S., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., Wang, L., Timsina, J., Western, D., Liu, M., Kohlfeld, P., Budde, J., Wilson, E. N., Guen, Y., Maurer, T. M., Haney, M., Yang, A. C., He, Z., Greicius, M. D., Andreasson, K. I., Sathyan, S., Weiss, E. F., Milman, S., Barzilai, N., Cruchaga, C., Wagner, A. D., Mormino, E., Lehallier, B., Henderson, V. W., Longo, F. M., Montgomery, S. B., Wyss-Coray, T. 2023; 624 (7990): 164-172


    Animal studies show aging varies between individuals as well as between organs within an individual1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

    View details for DOI 10.1038/s41586-023-06802-1

    View details for PubMedID 38057571

    View details for PubMedCentralID PMC10700136

  • Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer's disease. Science translational medicine Sung, Y. J., Yang, C., Norton, J., Johnson, M., Fagan, A., Bateman, R. J., Perrin, R. J., Morris, J. C., Farlow, M. R., Chhatwal, J. P., Schofield, P. R., Chui, H., Wang, F., Novotny, B., Eteleeb, A., Karch, C., Schindler, S. E., Rhinn, H., Johnson, E. C., Oh, H. S., Rutledge, J. E., Dammer, E. B., Seyfried, N. T., Wyss-Coray, T., Harari, O., Cruchaga, C. 2023; 15 (703): eabq5923


    Proteomic studies for Alzheimer's disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson's disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.

    View details for DOI 10.1126/scitranslmed.abq5923

    View details for PubMedID 37406134

  • Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease. Research square Cruchaga, C., Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Ali, M., Beric, A., Gorijala, P., Kohlfeld, P., Budde, J., Levey, A., Morris, J., Perrin, R., Ruiz, A., Marquié, M., Boada, M., de Rojas, I., Rutledge, J., Oh, H., Wilson, E., Guen, Y. L., Alvarez, I., Aguilar, M., Greicius, M., Pastor, P., Pulford, D., Ibanez, L., Wyss-Coray, T., Sung, Y. J., Phillips, B. 2023


    The integration of quantitative trait loci (QTL) with disease genome-wide association studies (GWAS) has proven successful at prioritizing candidate genes at disease-associated loci. QTL mapping has mainly been focused on multi-tissue expression QTL or plasma protein QTL (pQTL). Here we generated the largest-to-date cerebrospinal fluid (CSF) pQTL atlas by analyzing 7,028 proteins in 3,107 samples. We identified 3,373 independent study-wide associations for 1,961 proteins, including 2,448 novel pQTLs of which 1,585 are unique to CSF, demonstrating unique genetic regulation of the CSF proteome. In addition to the established chr6p22.2-21.32 HLA region, we identified pleiotropic regions on chr3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron-specificity and neurological development. We also integrated this pQTL atlas with the latest Alzheimer's disease (AD) GWAS through PWAS, colocalization and Mendelian Randomization and identified 42 putative causal proteins for AD, 15 of which have drugs available. Finally, we developed a proteomics-based risk score for AD that outperforms genetics-based polygenic risk scores. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.

    View details for DOI 10.21203/

    View details for PubMedID 37333337

    View details for PubMedCentralID PMC10275048

  • Cerebrospinal fluid immune dysregulation during healthy brain aging and cognitive impairment. Cell Piehl, N., van Olst, L., Ramakrishnan, A., Teregulova, V., Simonton, B., Zhang, Z., Tapp, E., Channappa, D., Oh, H., Losada, P. M., Rutledge, J., Trelle, A. N., Mormino, E. C., Elahi, F., Galasko, D. R., Henderson, V. W., Wagner, A. D., Wyss-Coray, T., Gate, D. 2022


    Cerebrospinal fluid (CSF) contains a tightly regulated immune system. However, knowledge is lacking about how CSF immunity is altered with aging or neurodegenerative disease. Here, we performed single-cell RNA sequencing on CSF from 45 cognitively normal subjects ranging from 54 to 82 years old. We uncovered an upregulation of lipid transport genes in monocytes with age. We then compared this cohort with 14 cognitively impaired subjects. In cognitively impaired subjects, downregulation of lipid transport genes in monocytes occurred concomitantly with altered cytokine signaling to CD8 Tcells. Clonal CD8T effector memory cells upregulated C-X-C motif chemokine receptor 6 (CXCR6) in cognitively impaired subjects. The CXCR6 ligand, C-X-C motif chemokine ligand 16 (CXCL16), was elevated in the CSF of cognitively impaired subjects, suggesting CXCL16-CXCR6 signaling as a mechanism for antigen-specific Tcell entry into the brain. Cumulatively, these results reveal cerebrospinal fluid immune dysregulation during healthy brain aging and cognitive impairment.

    View details for DOI 10.1016/j.cell.2022.11.019

    View details for PubMedID 36516855

  • Measuring biological age using omics data. Nature reviews. Genetics Rutledge, J., Oh, H., Wyss-Coray, T. 2022


    Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

    View details for DOI 10.1038/s41576-022-00511-7

    View details for PubMedID 35715611

  • Publisher Correction: Limited proteolysis-mass spectrometry reveals aging-associated changes in cerebrospinal fluid protein abundances and structures. Nature aging Shuken, S. R., Rutledge, J., Iram, T., Losada, P. M., Wilson, E. N., Andreasson, K. I., Leib, R. D., Wyss-Coray, T. 2022; 2 (5): 455

    View details for DOI 10.1038/s43587-022-00225-9

    View details for PubMedID 37118077

  • Limited proteolysis-mass spectrometry reveals aging-associated changes in cerebrospinal fluid protein abundances and structures (vol 2, pg 379, 2022) NATURE AGING Shuken, S. R., Rutledge, J., Iram, T., Losada, P., Wilson, E. N., Andreasson, K. I., Leib, R. D., Wyss-Coray, T. 2022; 2 (5): 455
  • Limited proteolysis–mass spectrometry reveals aging-associated changes in cerebrospinal fluid protein abundances and structures Nature Aging Shuken, S. R., Rutledge, J., Iram, T., Moran Losada, P., Wilson, E. N., Andreasson, K. I., Leib, R. D., Wyss-Coray, T. 2022
  • Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics. American journal of human genetics He, Z., Le Guen, Y., Liu, L., Lee, J., Ma, S., Yang, A. C., Liu, X., Rutledge, J., Losada, P. M., Song, B., Belloy, M. E., Butler, R. R., Longo, F. M., Tang, H., Mormino, E. C., Wyss-Coray, T., Greicius, M. D., Ionita-Laza, I. 2021


    Knockoff-based methods have become increasingly popular due to their enhanced power for locus discovery and their ability to prioritize putative causal variants in a genome-wide analysis. However, because of the substantial computational cost for generating knockoffs, existing knockoff approaches cannot analyze millions of rare genetic variants in biobank-scale whole-genome sequencing and whole-genome imputed datasets. We propose a scalable knockoff-based method for the analysis of common and rare variants across the genome, KnockoffScreen-AL, that is applicable to biobank-scale studies with hundreds of thousands of samples and millions of genetic variants. The application of KnockoffScreen-AL to the analysis of Alzheimer disease (AD) in 388,051 WG-imputed samples from the UK Biobank resulted in 31 significant loci, including 14 loci that are missed by conventional association tests on these data. We perform replication studies in an independent meta-analysis of clinically diagnosed AD with 94,437 samples, and additionally leverage single-cell RNA-sequencing data with 143,793 single-nucleus transcriptomes from 17 control subjects and AD-affected individuals, and proteomics data from 735 control subjects and affected indviduals with AD and related disorders to validate the genes at these significant loci. These multi-omics analyses show that 79.1% of the proximal genes at these loci and 76.2% of the genes at loci identified only by KnockoffScreen-AL exhibit at least suggestive signal (p < 0.05) in the scRNA-seq or proteomics analyses. We highlight a potentially causal gene in AD progression, EGFR, that shows significant differences in expression and protein levels between AD-affected individuals and healthy control subjects.

    View details for DOI 10.1016/j.ajhg.2021.10.009

    View details for PubMedID 34767756