# Single-cell and single-molecule profiling of protein-DNA interactions by MACHA > **NIH NIH R21** · UNIVERSITY OF CHICAGO · 2022 · $218,559 ## Abstract Summary Protein-DNA interactions are fundamental to DNA-dependent processes such as transcription, replication, DNA repair, and chromosome organization in all domains of cellular life. Enrichment-based methods to detect protein-DNA interactions are widely used to obtain genome-wide high-resolution maps of transcription factor binding and chromatin states. However, these methods have severe shortcomings when applied to single cells. In single-cell applications, enrichment-based methods cannot distinguish between unbound DNA and unsuccessful recovery of protein-bound DNA since both lead to absent DNA fragments and the sparse data makes it difficult or impossible to obtain cell-type-resolved binding profiles of transcription factors starting from complex tissues. This limitation significantly reduces the information content of single-cell data. MACHA will detect protein-DNA interactions in cells as cytosine methylation catalyzed by an exogenous GpC methyltransferase (GpCMTase) that is directed to protein-bound DNA by a primary antibody recognizing the protein of interest. ## Key facts - **NIH application ID:** 10593507 - **Project number:** 1R21HG012423-01A1 - **Recipient organization:** UNIVERSITY OF CHICAGO - **Principal Investigator:** Kohta Ikegami - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $218,559 - **Award type:** 1 - **Project period:** 2022-09-16 → 2024-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10593507 ## Citation > US National Institutes of Health, RePORTER application 10593507, Single-cell and single-molecule profiling of protein-DNA interactions by MACHA (1R21HG012423-01A1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10593507. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*