A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of ...localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .
The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in ...690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.
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•690,000 individual cells analyzed from 9 regions of adult mouse brain•RNA expression patterns corresponding to cell types, states, and locations•Transcriptional programs supporting axonal function and neuronal specializations•Online data and analysis resource “DropViz”
Sampling across multiple brain regions identifies hundreds of transcriptionally distinct groups of cells and reveals large-scale features of brain organization and neuronal diversity.
Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level ...of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.
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•Division of labor theory predicts distinct, mechanism-dependent spatial patterns•Spatial proximity of specialist cells suggests involvement of cell interactions•Archetype crosstalk networks reveal patterns of interactions between specialist cells•Enterocytes and fibroblasts exemplify distinct division of labor strategies
Two of the mechanisms that can drive division of labor in tissues are external signaling gradients and cell-cell interactions. Theory for optimal task allocation explains how to distinguish between the two strategies in single-cell and spatial omics data. This is demonstrated for colon fibroblasts, intestinal enterocytes, lung fibroblasts, and macrophages.
Studies on simulation input uncertainty are often built on the availability of input data. In this paper, we investigate an inverse problem where, given only the availability of output data, we ...nonparametrically calibrate the input models and other related performance measures of interest. We propose an optimization-based framework to compute statistically valid bounds on input quantities. The framework utilizes constraints that connect the statistical information of the real-world outputs with the input–output relation via a simulable map. We analyze the statistical guarantees of this approach from the view of data-driven distributionally robust optimization, and show how they relate to the function complexity of the constraints arising in our framework. We investigate an iterative procedure based on a stochastic quadratic penalty method to approximately solve the resulting optimization. We conduct numerical experiments to demonstrate our performances in bounding the input models and related quantities.
The Wnt/β-catenin signaling governs anterior-posterior neural patterning during development. Current human pluripotent stem cell (hPSC) differentiation protocols use a GSK3 inhibitor to activate Wnt ...signaling to promote posterior neural fate specification. However, GSK3 is a pleiotropic kinase involved in multiple signaling pathways and, as GSK3 inhibition occurs downstream in the signaling cascade, it bypasses potential opportunities for achieving specificity or regulation at the receptor level. Additionally, the specific roles of individual FZD receptors in anterior-posterior patterning are poorly understood. Here, we have characterized the cell surface expression of FZD receptors in neural progenitor cells with different regional identity. Our data reveal unique upregulation of FZD5 expression in anterior neural progenitors, and this expression is downregulated as cells adopt a posterior fate. This spatial regulation of FZD expression constitutes a previously unreported regulatory mechanism that adjusts the levels of β-catenin signaling along the anterior-posterior axis and possibly contributes to midbrain-hindbrain boundary formation. Stimulation of Wnt/β-catenin signaling in hPSCs, using a tetravalent antibody that selectively triggers FZD5 and LRP6 clustering, leads to midbrain progenitor differentiation and gives rise to functional dopaminergic neurons in vitro and in vivo.
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