Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue.

Studies on human parathyroids are generally limited to hyperfunctioning glands owing to the difficulty in obtaining normal human tissue. We therefore obtained non-human primate (NHP) parathyroids to provide a suitable alternative for sequencing that would bear a close semblance to human organs. Single-cell RNA expression analysis of parathyroids from four healthy adult M. mulatta reveals a continuous trajectory of epithelial cell states. Pseudotime analysis based on transcriptomic signatures suggests a progression from GCM2 hi progenitors to mature parathyroid hormone (PTH)-expressing epithelial cells with increasing core mitochondrial transcript abundance along pseudotime. We sequenced, as a comparator, four histologically characterized hyperfunctioning human parathyroids with varying oxyphil and chief cell abundance and leveraged advanced computational techniques to highlight similarities and differences from non-human primate parathyroid expression dynamics. Predicted cell-cell communication analysis reveals abundant endothelial cell interactions in the parathyroid cell microenvironment in both human and NHP parathyroid glands. We show abundant RARRES2 transcripts in both human adenoma and normal primate parathyroid cells and use coimmunostaining to reveal high levels of RARRES2 protein (also known as chemerin) in PTH-expressing cells, which could indicate that RARRES2 plays an unrecognized role in parathyroid endocrine function. The data obtained are the first single-cell RNA transcriptome to characterize nondiseased parathyroid cell signatures and to show a transcriptomic progression of cell states within normal parathyroid glands, which can be used to better understand parathyroid cell biology.

Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer.

Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information together, rather than siloing each technology into separate analysis pipelines. To advance this goal, we introduce a framework called the single-cell multi-modal generative adversarial network (scMMGAN) that integrates data from multiple modalities into a unified representation in the ambient data space for downstream analysis using a combination of adversarial learning and data geometry techniques. The framework's key improvement is an additional diffusion geometry loss with a new kernel that constrains the otherwise over-parameterized GAN. We demonstrate scMMGAN's ability to produce more meaningful alignments than alternative methods on a wide variety of data modalities and that its output can be used to draw conclusions from real-world biological experimental data.

Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference.

Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some. We propose building a model to make probabilistic predictions of HI using EI. Our feature mapping GAN (FMGAN), based on the conditional GAN framework, uses an embedding network to process conditions as part of the conditional GAN training to create manifold structure when it is not readily present in the conditions. We experiment on generating RNA sequencing of cell lines perturbed with a drug conditioned on the drug's chemical structure and generating FACS data from clinical monitoring variables on a cohort of COVID-19 patients, effectively describing their immune response in great detail.

Single cell immune profiling of dengue virus patients reveals intact immune responses to Zika virus with enrichment of innate immune signatures.

The genus Flavivirus contains many mosquito-borne human pathogens of global epidemiological importance such as dengue virus, West Nile virus, and Zika virus, which has recently emerged at epidemic levels. Infections with these viruses result in divergent clinical outcomes ranging from asymptomatic to fatal. Myriad factors influence infection severity including exposure, immune status and pathogen/host genetics. Furthermore, pre-existing infection may skew immune pathways or divert immune resources. We profiled immune cells from dengue virus-infected individuals by multiparameter mass cytometry (CyTOF) to define functional status. Elevations in IFNß were noted in acute patients across the majority of cell types and were statistically elevated in 31 of 36 cell subsets. We quantified response to in vitro (re)infection with dengue or Zika viruses and detected a striking pattern of upregulation of responses to Zika infection by innate cell types which was not noted in response to dengue virus. Significance was discovered by statistical analysis as well as a neural network-based clustering approach which identified unusual cell subsets overlooked by conventional manual gating. Of public health importance, patient cells showed significant enrichment of innate cell responses to Zika virus indicating an intact and robust anti-Zika response despite the concurrent dengue infection.

Interpretable Neuron Structuring with Graph Spectral Regularization.

While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden layers more interpretable without significantly impacting performance on the primary task. Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian penalty to structure the activations within a layer. This penalty encourages activations to be smooth either on a predetermined graph or on a feature-space graph learned from the data via co-activations of a hidden layer of the neural network. We show numerous uses for this additional structure including cluster indication and visualization in biological and image data sets.

LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS.

While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from one distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a new distribution not seen in training. Thus, they memorize the domain of the transformation, and cannot generalize the transformation out of sample. To address this, we propose a new neural network called a Neuron Transformation Network (NTNet) that isolates the signal representing the transformation itself from the other signals representing internal distribution variation. This signal can then be removed from a new dataset distributed differently from the original one trained on. We demonstrate the effectiveness of our NTNet on more than a dozen synthetic and biomedical single-cell RNA sequencing datasets, where the NTNet is able to learn the data transformation performed by genetic and drug perturbations on one sample of cells and successfully apply it to another sample of cells to predict treatment outcome.

Exploring single-cell data with deep multitasking neural networks.

It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE's various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue.

Identification and Analysis of Islet Antigen-Specific CD8+ T Cells with T Cell Libraries.

Type 1 diabetes (T1D) is most likely caused by killing of ß cells by autoreactive CD8+ T cells. Methods to isolate and identify these cells are limited by their low frequency in the peripheral blood. We analyzed CD8+ T cells, reactive with diabetes Ags, with T cell libraries and further characterized their phenotype by CyTOF using class I MHC tetramers. In the libraries, the frequency of islet Ag-specific CD45RO+IFN-γ+CD8+ T cells was higher in patients with T1D compared with healthy control subjects. Ag-specific cells from the libraries of patients with T1D were reactive with ZnT8186-194, whereas those from healthy control recognized ZnT8186-194 and other Ags. ZnT8186-194-reactive CD8+ cells expressed an activation phenotype in T1D patients. We found TCR sequences that were used in multiple library wells from patients with T1D, but these sequences were private and not shared between individuals. These sequences could identify the Ag-specific T cells on a repeated draw, ex vivo in the IFN-γ+ CD8+ T cell subset. We conclude that CD8+ T cell libraries can identify Ag-specific T cells in patients with T1D. The T cell clonotypes can be tracked in vivo with identification of the TCR gene sequences.