Author

N. Carlesso

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 3 Cell Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Organism:mouse· 2 Approach:statistical model· 2 Research Focus:trajectory inference· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Assay:CITE-seq· 1 Approach:deep learning· 1

Frequent Co-Authors

G. Marcucci· 3 shared S. Branciamore· 3 shared Y.-H. Kuo· 3 shared D. Maestrini· 2 shared D. O'Meally· 2 shared R. C. Rockne· 2 shared X. Wu· 2 shared A. Kumanogoh· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersCancer Biology

G. Marcucci

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Author profile →

4 papers

Recent·Most cited

Myeloid-biased HSC require Semaphorin 4A from the bone marrow niche for self-renewal under stress and life-long persistence

D. Toghani, S. Zeng, E. Mahammadov, E. I. Crosse, N. Seyedhassantehrani, C. Burns, D. Gravano, S. Radtke, H.-P. Kiem, S. Rodriguez, N. Carlesso, A. Pradeep, N. K. Wilson, S. J. Kinston, B. Gottgens, , L. Silberstein

This study identifies Sema4A as a critical niche factor required for myeloid-biased HSC self-renewal and long-term persistence under stress.

bioRxiv · Jan 12, 2022 · PreprintCell Biology

State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia

D. E. Frankhouser, D. O'Meally, S. Branciamore, L. Uechi, H. Qin, X. Wu, N. Carlesso, G. Marcucci, Y.-H. Kuo, R. C. Rockne

This study uses time-sequential miRNA expression data from a mouse AML model to identify state transitions during leukemogenesis and shows the model predicts time to AML development.

bioRxiv · Mar 2, 2021 · PreprintCancer Biology

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development

R. C. Rockne, S. Branciamore, J. Qi, D. Frankhouser, D. O'Meally, W.-K. Hua, G. J. Cook, L. Zhang, E. Carnahan, A. Marom, H. Wu, D. Maestrini, X. Wu, Y.-C. Yuan, Z. Liu, , G. Marcucci

The paper models leukemia transcriptome dynamics in a state-space to identify critical points that predict disease development.

bioRxiv · Jun 4, 2019 · PreprintCancer Biology

Aging in a relativistic biological space-time

D. Maestrini, D. Abler, V. Adhikarla, S. Armenian, S. Branciamore, N. Carlesso, G. Marcucci, Y.-H. Kuo, P. Sahoo, R. Rockne

This paper proposes a theoretical framework modeling biological aging as trajectories in a mathematical space-time, with applications to cancer and aging rates.

bioRxiv · Dec 5, 2017Cancer Biology