NeuroSC: Exploring Neurodevelopment via Spatiotemporal Collation of Anatomical Networks.

Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifying cell biological relationships in the connectome yields rich, multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSC an open source online platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome.

and encode micropeptides that regulate chromatin accessibility in vertebrate-specific neural cells.

Thousands of long intergenic non-coding RNAs (lincRNAs) are transcribed throughout the vertebrate genome. A subset of lincRNAs enriched in developing brains have recently been found to contain cryptic open-reading frames and are speculated to encode micropeptides. However, systematic identification and functional assessment of these transcripts have been hindered by technical challenges caused by their small size.

Single-cell analysis reveals inflammatory interactions driving macular degeneration.

Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease.

EMBEDDING SIGNALS ON GRAPHS WITH UNBALANCED DIFFUSION EARTH MOVER'S DISTANCE.

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph.

Immune cells and their inflammatory mediators modify β cells and cause checkpoint inhibitor-induced diabetes.

Checkpoint inhibitors (CPIs) targeting programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) have revolutionized cancer treatment but can trigger autoimmune complications, including CPI-induced diabetes mellitus (CPI-DM), which occurs preferentially with PD-1 blockade. We found evidence of pancreatic inflammation in patients with CPI-DM with shrinkage of pancreases, increased pancreatic enzymes, and in a case from a patient who died with CPI-DM, peri-islet lymphocytic infiltration. In the NOD mouse model, anti-PD-L1 but not anti-CTLA-4 induced diabetes rapidly.

MULTIMODAL DATA VISUALIZATION AND DENOISING WITH INTEGRATED DIFFUSION.

We propose a method called integrated diffusion for combining multimodal data, gathered via different sensors on the same system, to create a integrated 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 in data by maintaining low frequency eigenvectors of each modality both globally and locally. We show the utility of this integrated operator in denoising and visualizing multimodal toy data as well as multi-omic data generated from blood cells, measuring both gene expression and chromatin accessibility.

Multiscale PHATE identifies multimodal signatures of COVID-19.

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.

A reservoir of stem-like CD8 T cells in the tumor-draining lymph node preserves the ongoing antitumor immune response.

“Stem-like” TCF1 CD8 T (T) cells are necessary for long-term maintenance of T cell responses and the efficacy of immunotherapy, but, as tumors contain signals that should drive T cell terminal differentiation, how these cells are maintained in tumors remains unclear. In this study, we found that a small number of TCF1 tumor-specific CD8 T cells were present in lung tumors throughout their development. Yet, most intratumoral T cells differentiated as tumors progressed, corresponding with an immunologic shift in the tumor microenvironment (TME) from “hot” (T cell inflamed) to “cold” (non–T cell inflamed).

Diffusion Earth Mover's Distance and Distribution Embeddings.

We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover's Distance (EMD). We model the datasets as distributions supported on common data graph that is derived from the affinity matrix computed on the combined data. In such cases where the graph is a discretization of an underlying Riemannian closed manifold, we prove that Diffusion EMD is topologically equivalent to the standard EMD with a geodesic ground distance.

Structural and developmental principles of neuropil assembly in C. elegans.

Neuropil is a fundamental form of tissue organization within the brain, in which densely packed neurons synaptically interconnect into precise circuit architecture. However, the structural and developmental principles that govern this nanoscale precision remain largely unknown. Here we use an iterative data coarse-graining algorithm termed 'diffusion condensation' to identify nested circuit structures within the Caenorhabditis elegans neuropil, which is known as the nerve ring.

Coarse Graining of Data via Inhomogeneous Diffusion Condensation.

Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here, we consider multiple levels of abstraction via a multiresolution geometry of data points at different granularities. To construct this geometry we define a time-inhomogemeous diffusion process that effectively condenses data points together to uncover nested groupings at larger and larger granularities.

A Forward Chemical Genetic Screen Reveals Gut Microbiota Metabolites That Modulate Host Physiology.

The intestinal microbiota produces tens of thousands of metabolites. Here, we used host sensing of small molecules by G-protein coupled receptors (GPCRs) as a lens to illuminate bioactive microbial metabolites that impact host physiology. We screened 144 human gut bacteria against the non-olfactory GPCRome and identified dozens of bacteria that activated both well-characterized and orphan GPCRs, including strains that converted dietary histidine into histamine and shaped colonic motility; a prolific producer of the essential amino acid L-Phe, which we identified as an agonist for GPR56 and GPR97; and a species that converted L-Phe into the potent psychoactive trace amine phenethylamine, which crosses the blood-brain barrier and triggers lethal phenethylamine poisoning after monoamine oxidase inhibitor administration.

Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis.

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections.

Common risk alleles for inflammatory diseases are targets of recent positive selection.

Genome-wide association studies (GWASs) have identified hundreds of loci harboring genetic variation influencing inflammatory-disease susceptibility in humans. It has been hypothesized that present day inflammatory diseases may have arisen, in part, due to pleiotropic effects of host resistance to pathogens over the course of human history, with significant selective pressures acting to increase host resistance to pathogens. The extent to which genetic factors underlying inflammatory-disease susceptibility has been influenced by selective processes can now be quantified more comprehensively than previously possible.

An RNA profile identifies two subsets of multiple sclerosis patients differing in disease activity.

The multiple sclerosis (MS) patient population is highly heterogeneous in terms of disease course and treatment response. We used a transcriptional profile generated from peripheral blood mononuclear cells to define the structure of an MS patient population. Two subsets of MS subjects (MS(A) and MS(B)) were found among 141 untreated subjects.