Search

As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16(hi)CD66b(lo) neutrophil and IFN-γ(+) granzyme B(+) Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

Biological networks operate within architectural frameworks that influence the state and function of cells through niche-specific factors such as exposure to nutrients and metabolites, soluble signaling molecules, and direct cognate cell-cell communication. Spatial omics technologies incorporate environmental information into the study of biological systems, where the spatial coordinates of cells may directly or indirectly encode these micro-anatomical features. However, they suffer from technical artifacts, such as dropout, that impede biological discovery. Current methods that attempt to correct for this fail to adequately integrate highly informative spatial information when recovering gene expression and modelling cell-cell dynamics in situ. To address this oversight, we developed spatial Affinity-graph Recovery of Counts (spARC), a data diffusion-based filtration method that shares information between neighboring cells in tissue and related cells in expression space, to recover gene dynamics and simulate signalling interactions in spatial transcriptomics data. Following validation, we applied spARC to 10 IDH-mutant surgically resected human gliomas across WHO grades II-IV in order to study signaling networks across disease progression. This analysis revealed co-expressed genes that border the interface between tumor and tumor-infiltrated brain, allowing us to characterize global and local structure of glioma. By simulating paracrine signaling in silico, we identified an Osteopontin-CD44 interaction enriched in grade IV relative to grade II and grade III astrocytomas, and validated the clinical relevance of this signaling axis using TCGA.

We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on the same system, to create a joint data diffusion operator. As real world data suffers from both local and global noise, we introduce mechanisms to optimally calculate a diffusion operator that reflects the combined information from both modalities. We show the utility of this joint operator in data denoising, visualization and clustering, performing better than other methods to integrate and analyze multimodal data. We apply our method to multi-omic data generated from blood cells, measuring both gene expression and chromatin accessibility. Our approach better visualizes the geometry of the joint data, captures known cross-modality associations and identifies known cellular populations. More generally, integrated diffusion is broadly applicable to multimodal datasets generated in many medical and biological systems.

The International Year of the Periodic Table, proclaimed by the United Nations to begin January 2019, coincides with the one hundred and fiftieth anniversary of Mendeleev’s published Periodic Table. The Periodic Table marked the coming of age of Chemistry. Mendeleev’s genius lay not in the discovery of the fact of periodicity, but in his interpretation of it as a fundamental principle, allowing for concrete hypotheses to be tested. His predictions of the properties of elements-tobe, as well as presentation in a simple and easy to understand chart were contributing factors to make it his lasting legacy. The success of the Periodic Table was a triumph of the value of understanding chemistry based on theory over merely depending on empirical observations and an ability to relate such theory to experiments. Mendeleev’s discovery emerged out of the difficulties he encountered in teaching chemistry and, interestingly enough, it continues to serve that purpose today. The Periodic Table is taught worldwide early on in science education. Regardless of scientific technicalities, the Periodic Table will always stand as a symbol of the beauty in the simplicity of nature, an order that permeates a seemingly chaotic world of elements with deep scientific and philosophical underpinnings. In this article, we trace the origins of this epoch-making discovery, his life as well as the times in which Mendeleev lived and worked and the present and future impact of his discovery.

Nipah Virus (NiV) has been designated as a priority disease with an urgent need for therapeutic development by World Health Organization. The monoclonal antibody m102.4 binds to the immunodominant NiV receptor-binding glycoprotein (GP), and potently neutralizes NiV, indicating its potential as a therapeutic agent. Although the co-crystal structure of m102.3, an m102.4 derivative, in complex with the GP of the related Hendra Virus (HeV) has been solved, the structural interaction between m102.4 and NiV is uncharacterized. Herein, we used structure-guided alanine-scanning mutagenesis to map the functional epitope and paratope residues that govern the antigen–antibody interaction. Our results revealed that the binding of m102.4 is mediated predominantly by two residues in the HCDR3 region, which is unusually small for an antibody-antigen interaction. We performed computational docking to generate a structural model of m102.4-NiV interaction. Our model indicates that m102.4 targets the common hydrophobic central cavity and a hydrophilic rim on the GP, as observed for the m102.3-HeV co-crystal, albeit with Fv orientation differences. In summary, our study provides insight into the m102.4-NiV interaction, demonstrating that structure-guided alanine-scanning and computational modeling can serve as the starting point for additional antibody reengineering (e.g. affinity maturation) to generate potential therapeutic candidates.

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. Here we present Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Our approach creates a tree of data granularities that can be cut at coarse levels for high level summarizations, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Our analysis identifies pathogenic cellular populations, CD16-hiCD66b-lo neutrophils and IFNγ+GranzymeB+ Th17 cells, and shows that cellular groupings discovered by Multiscale PHATE are directly predictive of disease outcome. We use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another on patient clinical features, both associating immune subsets and clinical markers with outcome. Conflict of Interest: Dr. Krishnaswamy is on the scientific advisory board of KovaDx and AI Therapeutics. Dr. Iwasaki a member of the SAB for InProTher. Dr. Iwasaki is a co-founder of RIGImmune. Dr. Wilson is founder of Efference. Dr. Ko is a member of the expert panel of the Reckit Global Hygiene Institute. The remaining authors have no competing interests to declare. Ethical Approval: This study was approved by Yale Human Research Protection Program Institutional Review Boards (FWA00002571, protocol ID 2000027690). Informed consent was obtained from all enrolled patients and healthcare workers.