SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics

Spatial Transcriptomics (ST) is a method that captures spatial gene expression profiles within histological sections. The discrete spatial distribution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability of Implicit Neural Representations (INRs) that can improve both the spatial resolution and the gene expression. Concretely within the proposed SUICA, we incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots and provide informative embeddings that are structure-aware for spatial mapping. We also tackle the extremely skewed distribution in a regression-by-classification fashion and enforce classification-based loss functions for the optimization of SUICA. By extensive experiments of a wide range of common ST platforms, SUICA outperforms both conventional INR variants and SOTA methods for ST super-resolution regarding numerical fidelity, statistical correlation, and bio-conservation. The prediction by SUICA also showcases amplified gene signatures that enriches the bio-conservation of the raw data and benefits subsequent analysis. The code is available at https://github.com/Szym29/SUICA.

Anatomical basis of sex differences in human post-myocardial infarction ECG phenotypes identified by novel automated torso-cardiac 3D reconstruction

The electrocardiogram (ECG) is routinely used in cardiology, though its interpretation is confounded by anatomical variability. A novel, automated computational pipeline enables quantification of torso-ventricular anatomy metrics from magnetic resonance imaging, and comparison to ECG characteristics. Sex and myocardial infarction differences are investigated based on 1051 healthy and 425 post-MI subjects from UK Biobank. Smaller ventricles in females explain ~50% of shorter QRS durations than in males, and contribute to lower STJ amplitudes in females (also due to more superior and posterior position). In females, torso-ventricular anatomy, particularly from larger BMI, is a stronger modulator of T wave amplitude reductions and left-deviated R axis angles in post-MI than in males. Thus, female MI phenotype is less reflective of pathology, and baseline STJ amplitudes and QRS durations are further from clinical thresholds. Therefore, quantification of anatomical sex-differences and impact on ECG in health and disease is critical to avoid clinical sex-bias.