Biology subjects

Emanuel, G.

Publications and source records attributed to Emanuel, G..

3 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2022

Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve single-cell resolution, directly mapping single cell identities to spatial positions. MERFISH produces an intrinsically different data type than scRNA-seq and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas as well as from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that compared to scRNA-seq, MERFISH provides a quantitatively comparable method for measuring single-cell gene expression and can robustly identify cell types without the need for computational integration with scRNA-seq reference atlases.

bioinformatics↗

bioRxiv2017

High-throughput, image-based screening of genetic variant libraries

Image-based, high-throughput, high-content screening of pooled libraries of genetic perturbations will greatly advance our understanding biological systems and facilitate many biotechnology applications. Here we introduce a high-throughput screening method that allows highly diverse genotypes and the corresponding phenotypes to be imaged in numerous individual cells. To facilitate genotyping by imaging, barcoded genetic variants are introduced into the cells, each cell carrying a single genetic variant connected to a unique, nucleic-acid barcode. To identify the genotype-phenotype correspondence, we perform live-cell imaging to determine the phenotype of each cell, and massively multiplexed FISH imaging to measure the barcode expressed in the same cell. We demonstrated the utility of this approach by screening for brighter and more photostable variants of the fluorescent protein YFAST. We imaged 20 million cells expressing ~60,000 YFAST mutants and identified novel YFAST variants that are substantially brighter and/or more photostable than the wild-type protein.

systems biology↗