Shiau, C. | Search bioRxiv

bioRxiv2023

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

cancer biology↗

bioRxiv2022

Global statistical models of protein coevolution reveal higher-order sectors beyond those obtained from structure alone

1Recent methods have shown promise in using pairwise sequence coevolution predictions to illuminate physical interactions and functional relationships between pairs of protein residues. As a result, there has been an increased interest in identifying higher-order correlations between sequence positions in an effort to further understand how the multiple sequence alignment (MSA) encodes the conserved biological properties of a protein family. To this end, we propose a robust and generalizable spectral clustering model that can extract interconnected networks of coevolving residues - termed "protein sectors" - using pairwise sequence coevolution predicted by a global statistical model. We assess the statistical and evolutionary origins for protein sectors extracted from the MSA for 120 protein families. We show that protein sectors are extracted from a subset of densely connected components in the sequence coevolution matrix, many of which are not present in the pairwise residue contact graph that is constructed from the protein crystal structure, revealing the existence of networks of paired residues that are not necessarily in direct physical contact but are nonetheless evolutionarily coupled. We found that protein sectors form structurally connected entities in three-dimensional space, despite sector identification being independent of protein crystal structure. Interestingly, protein families with high structural similarity do not share similar protein sectors, suggesting that nuances in the sequence coevolution matrix can differentiate between the evolutionary histories of structurally-related protein families.

bioinformatics↗