Pilzer, D. | bioRxiv Science

bioRxiv2021

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation

The function of mammalian cells is largely influenced by their tissue microenvironment. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function by enabling a transcriptome-wide evaluation of gene expression in situ. A critical limitation of the current technologies, however, is that their resolution is limited to niches (spots) of sizes well beyond that of a single cell, thus providing measurements for cell aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single-cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyses are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states that cannot be clearly demarcated but reflects important differences in the way cells function and interact with their surroundings. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi-resolution analysis for spatial transcriptomics that explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods and that it can estimate gene expression by every cell type inside every spot. We then introduce an automated pipeline that uses DestVI for analysis of single tissue slices and comparison between tissues. We apply this pipeline to study the immune crosstalk within lymph nodes to infection and explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a high resolution and accurate spatial characterization of the cellular organization of these tissues, and that it is capable of identifying important cell-type-specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software package in the scvi-tools codebase (https://scvi-tools.org).

bioinformatics↗

bioRxiv2017

Unmasking cellular response of a bloom-forming alga to virus infection by resolving expression profiling at a single-cell level

Marine viruses are major evolutionary and biogeochemical drivers of microbial life in the ocean. Host response to viral infection typically includes virus-induced rewiring of metabolic network to supply essential building blocks for viral assembly, as opposed to activation of anti-viral host defense. Nevertheless, there is a major bottleneck to accurately discern between viral hijacking strategies and host defense responses when averaging bulk population response. Here we use Emiliania huxleyi, a bloom-forming alga and its specific virus (EhV), as one of the most ecologically important host-virus model system in the ocean. Using automatic microfluidic setup to capture individual algal cells, we quantified host and virus gene expression on a single-cell resolution during the course of infection. We revealed high heterogeneity in viral gene expression among individual cells. Simultaneous measurements of expression profiles of host and virus genes at a single-cell level allowed mapping of infected cells into newly defined infection states and uncover a yet unrecognized early phase in host response that occurs prior to viral expression. Intriguingly, resistant cells emerged during viral infection, showed unique expression profiles of metabolic genes which can provide the basis for discerning between viral resistant and sensitive cells within heterogeneous populations in the marine environment. We propose that resolving host-virus arms race at a single-cell level will provide important mechanistic insights into viral life cycles and will uncover host defense strategies.

ecology↗