Biomedical subjects

Alon Goren

Publications and source records attributed to Alon Goren.

3 recordsLinked to original sources

PubMed2026

Improved spike-in normalization clarifies the relationship between active histone modifications and transcription.

Spike-in normalization enables quantitative analysis of chromatin immunoprecipitation sequencing (ChIP-seq) signal. Here we introduce a robust dual spike-in normalization approach for ChIP-seq (ChIP-wrangler), optimize parameters and verify its accuracy in quantifying changes in ChIP-seq signal and detecting technical artifacts. We use ChIP-wrangler to revisit recent claims that active histone marks depend on transcription. We show that acute depletion of RNA polymerase II (RNAPII) has a modest impact on H3K27ac levels, with only 6% of peaks significantly changing after RNAPII depletion, indicating that histone acetylation maintenance is not entirely dependent on ongoing transcription. Promoters and enhancers are differentially affected, with 82% of decreasing acetylation peaks located at promoter-distal elements with enhancer-related motifs. ChIP-wrangler provides increased rigor and 'guardrails' for successful spike-in normalization and, as applied here, refines the understanding of crosstalk between RNAPII activity and transcription-associated histone marks.

Histones↗

PubMed2025

Automated chromatin profiling with spa-ChIP-seq uncovers the impacts of condition variations.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to study the genomic localization of DNA-associated proteins. However, conventional protocols include multiple manual steps that can introduce inconsistency and limit scalability, thereby restricting the inclusion of appropriate replicates and controls. Although the introduction of liquid handling platforms has improved reproducibility, most existing efforts have automated only a subset of the workflow, and extending automation to efficiently map non-histone proteins, such as chromatin regulators, remains challenging. Here, we present a fully automated implementation of our previously developed single-pot ChIP-seq protocol (Texari et al. 2021), named spa-ChIP-seq, which enables scalable processing of 8 to 96 ChIP-seq samples from crosslinked cells to sequencing-ready library in approximately three days with an estimated cost of $70 per sample. Benchmarking spa-ChIP-seq against manual ChIP-seq performed in parallel demonstrates comparable signal-to-noise ratio between the two workflows. Using spa-ChIP-seq, we systematically evaluate multiple parameters including shearing and crosslinking conditions, buffer compositions, and the ratio of antibody to cell-number. We find, for the first time to our knowledge, that weaker genomic localization signals are sensitive to changing the antibody to cell-number ratio, whereas the stronger signals remain unaffected. This finding underscores the importance of maintaining consistent antibody-to-cell-number ratio for comparative studies, such as treatment responses or chromatin-QTL mapping. The spa-ChIP-seq protocol is publicly available, including deck setups, operational parameters, and scripts. We envision that this robust, cost-efficient protocol will facilitate high-throughput, reproducible ChIP-seq analyses, supporting large-scale studies of antibody validation, compound screening, population genomics, and diagnostic frameworks.

Journal Article↗

PubMed2025

Genome editing with programmable base editors in human cells.

Genome editing has garnered significant attention over the last decade, resulting in a massive expansion of the genome engineering toolbox. Base editors encompass a class of tools that enable installing single-nucleotide changes in genomic DNA without the use of double-strand breaks. With the ever-increasing development of new and/or improved base editor systems, it is easy to be overwhelmed by the abundance of options. Here, we provide clear guidance to facilitate the selection of a base editor and to design guide RNAs (gRNAs) to suit various needs. Additionally, we describe in detail how to generate gRNA plasmids, transfect various mammalian cell types, and evaluate editing efficiencies. Finally, we give alternative methods and troubleshooting tips for some common pitfalls encountered during base editing.

Humans↗