SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.

Motivation: Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells.

Results: To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

Background: Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals.

Results: Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data.

STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping.

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data.

Alignment of spatial transcriptomics data using diffeomorphic metric mapping.

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping.

The genetic insulator RiboJ increases expression of insulated genes.

A primary objective of synthetic biology is the construction of genetic circuits with behaviors that can be predicted based on the properties of the constituent genetic parts from which they are built. However a significant issue in the construction of synthetic genetic circuits is a phenomenon known as context dependence in which the behavior of a given part changes depending on the choice of adjacent or nearby parts. Interactions between parts compromise the modularity of the circuit, impeding the implementation of predictable genetic constructs.