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NIH
Forecasted

NOT-RM-25-019

Cellular Senescence Network (SenNet): SenNet Data Coordination, Integration and Organizational Center (U24 or UM1)

Summary

AI-generated

Cellular Senescence Network (SenNet) Program – Stage 2

The NIH Common Fund, National Institute on Aging, and National Cancer Institute are launching Stage 2 of the Cellular Senescence Network (SenNet) to map senescent cell heterogeneity across human tissues and identify tissue-specific senescence biomarkers that can guide development of senotherapeutic interventions. This coordinated effort seeks to understand how senescent cells contribute to health and disease across the lifespan, with particular focus on age-associated diseases. The program will generate Atlases of Senescence integrating spatial transcriptomics and tissue mapping data to reveal the biological and functional landscape of senescence markers in normal tissue, aging, and disease states. A Data Coordination, Integration and Organizational Center (DCIOC) will serve as the consortium hub, consolidating multi-site tissue mapping efforts and managing outreach and collaboration across the network.

The program targets researchers with expertise in cellular senescence biology, innovative technologies for senescent cell identification and characterization, computational approaches to analyze senescence data, and in vivo strategies for targeting senescent cells. Applications spanning computational biology, spatial genomics, tissue-level analysis, and clinical translation are encouraged.

  • Who can apply: Institutions and research teams with expertise in cellular senescence, senescence biomarker development, tissue mapping, or senotherapeutic approaches; collaborative consortia preferred.
  • Funding & project length: Not stated.
  • Award mechanism: U24 (Data Coordination Center) or UM1 (consortium/cooperative agreement).
  • Key dates: Notice of Funding Opportunity (NOFO) publication date not yet announced; applications not currently being solicited.
  • Best fit for: Computational biologists, gerontologists, and translational researchers developing senescence atlases, spatial transcriptomics methods, or senescence-targeted therapeutics in aging and age-related disease.

Insights (6)

Spatial transcriptomics and tissue mapping expertise directly aligns with atlas-building mission

strategic fit

SenNet Stage 2 explicitly prioritizes integrating tissue mapping with senescence biomarkers to generate atlases incorporating spatial distribution across tissues. Applicants with established spatial transcriptomics platforms, tissue dissociation/preservation protocols, or computational integration pipelines will be highly competitive. Preliminary data demonstrating tissue-specific senescence marker identification in human biospecimens is a strong differentiator.

Consortium structure requires multi-institutional partnerships and data coordination commitment

collaboration

The DCIOC will serve as the organizational hub managing consortium activities, indicating this is a coordinated network program rather than independent grants. Successful applicants should plan for significant collaborative obligations, data sharing requirements, and alignment with a centralized atlas-building effort. Solo investigators or those uncomfortable with consortium governance should reconsider.

Computational biology and senescence atlas integration are core program priorities

strategic fit

The NOFO explicitly seeks investigators with expertise in computational tools to identify and characterize senescent cells, and the DCIOC will integrate tissue mapping with biological/functional data. Applicants combining wet-lab senescence characterization with bioinformatics, machine learning for biomarker discovery, or atlas visualization will strengthen competitiveness. Preliminary computational pipelines or proof-of-concept atlas modules are valuable preliminary data.

U24/UM1 mechanisms signal infrastructure and coordination expectations, not traditional R01 scope

eligibility

U24 (cooperative agreement) and UM1 (cooperative agreement) mechanisms emphasize infrastructure, data coordination, and consortium management rather than hypothesis-driven research. Applicants should frame projects around building shared resources, generating public datasets, and supporting the broader SenNet network—not individual discovery. This shifts the value proposition from novel findings to enabling tools and data.

Established research programs with consortium leadership experience are advantaged

career stage

The emphasis on organizational hub functions, data coordination, and consortium management suggests preference for investigators with prior experience leading multi-institutional efforts or managing large datasets. Early-stage investigators may be competitive as collaborators or co-investigators but face disadvantages as primary applicants unless they partner with established consortium leaders.

Broad senescence expertise requirement limits but does not eliminate competition

competition

The specificity of senescence biology combined with the requirement for tissue mapping, biomarker development, and computational integration narrows the applicant pool compared to general aging research. However, the Common Fund and multi-IC support (NIA, NCI, others) suggests substantial funding and multiple anticipated awards. Competition will be intense among qualified applicants but manageable for those with genuine senescence expertise.

Key Facts

Deadline

Posted

Wed, May 28, 2025

Expected Awards

25

U24
UM1
93.310
Grants.gov

Keywords

senescent cells
cellular senescence
senescence biomarkers
tissue mapping
spatial transcriptomics
age-associated diseases
human biospecimens
senescence atlases
computational biology
tissue-specific markers
senotherapeutics
senescent cell heterogeneity
aging biology

Research Areas

NIH Institute
General Medical SciencesNIGMS
OpenAlex
Life SciencesD1Physical SciencesD3Health SciencesD4
Fields
Biochemistry, Genetics & Molecular BiologyF13Computer ScienceF17Immunology & MicrobiologyF24MathematicsF26MedicineF27NeuroscienceF28Pharmacology, Toxicology & PharmaceuticsF30Health ProfessionsF36
Subfields
AgingS1302Cancer ResearchS1306Cell BiologyS1307Molecular BiologyS1312Molecular MedicineS1313Artificial IntelligenceS1702Computer Science ApplicationsS1706Computer Vision & Pattern RecognitionS1707Applied MathematicsS2604Modeling & SimulationS2611Statistics & ProbabilityS2613OncologyS2730Cellular & Molecular NeuroscienceS2804Drug DiscoveryS3002PharmacologyS3004
Topics
Cancer Immunotherapy and BiomarkersT10158Advanced biosensing and bioanalysis techniquesT10207Genomics and Chromatin DynamicsT10222Developmental Biology and Gene RegulationT10268Epigenetics and DNA MethylationT10269Cell death mechanisms and regulationT10294Mitochondrial Function and PathologyT10301Cancer Cells and MetastasisT10336+19 more
MeSH
AnatomyA
Nervous SystemA08TissuesA10CellsA11Hemic & Immune SystemsA15
OrganismsB
DiseasesC
NeoplasmsC04Nervous System DiseasesC10Cardiovascular DiseasesC14Nutritional & Metabolic DiseasesC18Endocrine System DiseasesC19Pathological Conditions & SymptomsC23
Chemicals & DrugsD
Macromolecular SubstancesD05Amino Acids, Peptides & ProteinsD12Biological FactorsD23
Analytical/Diagnostic/Therapeutic TechniquesE
DiagnosisE01Investigative TechniquesE05
Phenomena & ProcessesG
Cell PhysiologyG04Genetic PhenomenaG05Biological PhenomenaG16
Disciplines & OccupationsH
Natural Science DisciplinesH01
Information ScienceL
Information ScienceL01
Health CareN
Population CharacteristicsN01Health Care Quality & EvaluationN05
ANZSRC FoR
Biological Sciences31
Biochemistry & Cell Biology3101Genetics3105
Biomedical & Clinical Sciences32
Clinical Sciences3202Immunology3204Medical Biochemistry & Metabolomics3205Neurosciences3209Oncology & Carcinogenesis3211
Health Sciences42
Epidemiology4202Public Health4206
Information & Computing46
Artificial Intelligence4602Data Management & Data Science4605Machine Learning4611
Mathematical Sciences49
Numerical & Computational Mathematics4903Statistics4905

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