Biology subjects

Aditham, A.

Publications and source records attributed to Aditham, A..

3 recordsLinked to original sources

bioRxiv2023

High-throughput thermodynamic and kinetic measurements of transcription factor/DNA mutations reveal how conformational heterogeneity can shape motif selectivity

Transcription factors (TFs) bind DNA sequences with a range of affinities, yet the mechanisms determining energetic differences between high- and low-affinity sequences ( selectivity) remain poorly understood. Here, we investigated two basic helix-loop-helix TFs, MAX (H. sapiens) and Pho4 (S. cerevisiae), that bind the same high-affinity sequence with highly similar nucleotide-contacting residues and bound structures but are differentially selective for non-cognate sequences. By measuring >1700 Kds and >500 rate constants for Pho4 and MAX mutant libraries binding multiple DNA sequences and comparing these measurements with thermodynamic and kinetic models, we identify the biophysical mechanisms by which changes to TF sequence alter both bound and unbound conformational ensembles to shape specificity landscapes. These results highlight the importance of conformational heterogeneity in determining sequence specificity and selectivity and can guide future efforts to engineer nucleic acid-binding proteins with enhanced selectivity.

biophysics↗

bioRxiv2022

Spatially Resolved Single-cell Translatomics at Molecular Resolution

The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolutions. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation for thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in the adult mouse brain tissue with a spatial cell atlas of 62,753 cells. This spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.

genomics↗

bioRxiv2021

ClusterMap: multi-scale clustering analysis of spatial gene expression

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

genomics↗