Author

Zachary Niziolek

Harvard UniversityORCID 0000-0002-7361-9686Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Organism:mouse· 3 Research Focus:differential expression· 2 Approach:experimental assay· 2 Research Focus:gene regulatory network inference· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Bogdan BudnikiD· 2 shared Aviv RegeviD· 1 shared Brendan T. InnesiD· 1 shared Brittany A. MayweatheriD· 1 shared Ceren OzekiD· 1 shared Danielle Ashley DionneiD· 1 shared David H. PerlmaniD· 1 shared Edward EmmottiD· 1 shared

Activity by Year

2018-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersNeuroscience

Bogdan BudnikORCID

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3 papers

Recent·Most cited

NeuroscienceFrontiers in Molecular NeurosciencePeer-reviewedAug 7, 2023

Brain cell type specific proteomics approach to discover pathological mechanisms in the childhood CNS disorder mucolipidosis type IV

Madison Sangster, Sanjid Shahriar, Zachary Niziolek…·Yulia Grishchuk

The paper uses cell type-specific proteomics in a MLIV mouse model to identify molecular mechanisms and dysregulated pathways in different brain cell types.

11 citations

mouseproteomics

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NeuroscienceNature NeurosciencePeer-reviewedSep 24, 2019

Single-cell transcriptomics of the aged mouse brain reveals convergent, divergent and unique aging signatures

Methodios Ximerakis, Scott Lipnick, Brendan T. Innes…·Lee L. Rubin

This study uses single-cell transcriptomics to analyze aged mouse brains, identifying cell-type-specific aging signatures and highlighting molecular processes like ribosomal biogenesis.

736 citationstop 1%

mousescRNA-seq

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Systems BiologybioRxivPreprintAug 25, 2018

Automated sample preparation for high-throughput single-cell proteomics

Harrison Specht, Guillaume Harmange, David H. Perlman…·Nikolai Slavov

Minimal ProteOmic sample Preparation (mPOP) eliminates cleanup losses in single-cell mass spectrometry proteomics, enabling automated SCoPE-MS profiling that separates mammalian cells by cell type and cell-division-cycle phase.

77 citations

mousehuman

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