In silico tissue generation and power analysis for spatial omics.

As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization.

Discovery and generalization of tissue structures from spatial omics data.

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.

Cardiovascular adjustments during anticipated postural changes.

It is well-documented that feedforward cardiovascular responses occur at the onset of exercise, but it is unclear if such responses are associated with other types of movements. In this study, we tested the hypothesis that feedforward cardiovascular responses occur when a passive (imposed) 60° head-up tilt is anticipated, such that changes in heart rate and carotid artery blood flow (CBF) commence prior to the onset of the rotation. A light cue preceded head-up tilts by 10 sec, and heart rate and CBF were determined for 5-sec time periods prior to and during tilts. Even after these stimuli were provided for thousands of trials spanning several months, no systematic changes in CBF and heart rate occurred prior to tilts, and variability in cardiovascular adjustments during tilt remained substantial over time. We also hypothesized that substitution of 20° for 60° tilts in a subset of trials would result in exaggerated cardiovascular responses (as animals expected 60° tilts), which were not observed. These data suggest that cardiovascular adjustments during passive changes in posture are mainly elicited by feedback mechanisms, and that anticipation of passive head-up tilts does not diminish the likelihood that a decrease in carotid blood flow will occur during the movements.

Comparative Transcriptomics Among Four White Pine Species.

Conifers are the dominant plant species throughout the high latitude boreal forests as well as some lower latitude temperate forests of North America, Europe, and Asia. As such, they play an integral economic and ecological role across much of the world. This study focused on the characterization of needle transcriptomes from four ecologically important and understudied North American white pines within the Pinus subgenus Strobus The populations of many Strobus species are challenged by native and introduced pathogens, native insects, and abiotic factors. RNA from the needles of western white pine (Pinus monticola), limber pine (Pinus flexilis), whitebark pine (Pinus albicaulis), and sugar pine (Pinus lambertiana) was sampled, Illumina short read sequenced, and de novo assembled. The assembled transcripts and their subsequent structural and functional annotations were processed through custom pipelines to contend with the challenges of non-model organism transcriptome validation. Orthologous gene family analysis of over 58,000 translated transcripts, implemented through Tribe-MCL, estimated the shared and unique gene space among the four species. This revealed 2025 conserved gene families, of which 408 were aligned to estimate levels of divergence and reveal patterns of selection. Specific candidate genes previously associated with drought tolerance and white pine blister rust resistance in conifers were investigated.