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Biology subjects

Revuru, S.

Publications and source records attributed to Revuru, S..

2 recordsLinked to original sources

MERFISH 2.0, an ultra-sensitive single-cell spatial transcriptomics imaging chemistry across diverse tissue types

Spatial transcriptomics has emerged as a transformative approach for elucidating tissue architecture, cellular heterogeneity, and disease mechanisms by preserving the spatial context of gene expression in cells. Despite these advances, many spatial transcriptomic methods underperform in archival or clinically relevant specimens, particularly formalin-fixed, paraffin-embedded (FFPE) tissues, where RNA degradation and crosslinking hinder transcript detection. To address these challenges, we developed Multiplexed Error Robust Fluorescence In Situ Hybridization 2.0 (MERFISH 2.0), an optimized spatial transcriptomic imaging chemistry to enhance profiling of fragmented and highly crosslinked RNA. Across diverse human and mouse tissues preserved as fresh-frozen, fixed-frozen, and FFPE specimens, MERFISH 2.0 substantially increased transcript detection sensitivity by up to [~]8-fold relative to MERFISH 1.0 while preserving quantitative concordance (Pearson r [≥] 0.8 across tissues). In archived fresh-frozen human brain samples, MERFISH 2.0s enhanced sensitivity improved transcript recovery, enhanced cell type resolution and spatial analyses. In low quality archival FFPE human breast cancer specimen, MERFISH 2.0 revealed additional cell populations, novel cell clusters, refined tumor-immune architecture, and increased detection of gene-gene and cell-cell interactions relative to MERFISH 1.0, underscoring the impact of improved sensitivity on downstream spatial analysis. By substantially expanding robust transcript detection to degraded and archival samples, MERFISH 2.0 enables scalable, cohort-level spatial transcriptomic analysis across clinically relevant tissue collections.

genomics↗

Modelling complex growth profiles of Bacteroides fragilis and Escherichia coli on various carbohydrates in an anaerobic environment

Previously published models for microbial growth focus only on either death or growth and are unable to account for differently shaped growth curves. Currently, there is no model capable of incorporating combinations of microbial growth trends. This study creates a bacterial growth model that incorporates growth, death, lag, and tail phases as well as applies this model to the growth trends of Bacteroides fragilis and Escherichia coli on 13 different carbohydrate substances. Growth trends were collected by measuring the optical densities over 72 hours for either B. fragilis or E. coli in a chemically defined media supplemented by a mono- or disaccharide. The Digital Environment to Enable Data-driven Science (DEEDS) platform was utilized to parse data and apply the developed model to obtain parameter values. E. coli was found to grow on the chemically defined media alone while B. fragilis was unable to grow on it alone. E. coli growth was led by 10 mM concentration of substrates while B. fragilis growth was substrate dependent. Bacterial death only occurred for B. fragilis but was found to be dependent on concentration for the two most significant substrates. A singular model was developed that does not require prior knowledge of metabolomics and is capable of incorporating a combination of growth and death trends.

microbiology↗