Evidence map›Paper›PMID 40257567›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Identification of Chromatin Readers Using a Peptide Pull-Down and Mass Spectrometry Integrated Approach.

Stephanie Stransky, Jazmine Saskya-Joseph, Yan Sun, Simone Sidoli

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stephanie StranskyDepartment of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA.
Jazmine Saskya-JosephDepartment of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA.
Yan SunDepartment of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA.
Simone SidoliDepartment of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA. simone.sidoli@einsteinmed.org.

Funding

Orbitrap Exploris 480 Basic SystemS10OD030286 · OD · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI SIDOLI, SIMONE · 2021 to 2021
$600k
NIH HHS S10 OD030286
6 · The paper itself

Abstract

Histone proteins regulate essential cellular processes by being decorated with a myriad of posttranslational modifications (PTMs). These processes are mostly led by the ability of these PTMs to recruit protein readers involved in gene transcription, DNA replication, DNA damage, chromatin remodeling, and other functions. Identifying histone readers is critical for the understanding of mechanisms leading to these functions and potentially predict targets for treatment in anomalous phenotypes. For this reason, histone reader identification has been performed for several years using strategies aiming to increase depth, resolution, and accuracy, e.g., ChIP-MS, proximity biotinylation, photo-crosslinking, and array-based technologies. In this chapter, we describe a protocol for identifying histone readers in a straightforward and unbiased manner: peptide pull-down combined with high-resolution mass spectrometry. Synthesized and immobilized modified histone peptides are incubated with nuclear extracts, and the PTMs' interactors are analyzed by mass spectrometry, which allows the identification of thousands of proteins with high confidence. We also describe the steps for a proteomic data analysis and present tools for a comprehensive data integration.

Indexed as

ChromatinHistonesMass SpectrometryPeptidesProteomicsHumansProtein Processing, Post-TranslationalChromatinHistonesPeptidesChromatinMass spectrometryPeptide pull–downProtein–protein interactionsProteomics

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.