8 research outputs found
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings
Comparing unpaired samples of a distribution or population taken at different
points in time is a fundamental task in many application domains where
measuring populations is destructive and cannot be done repeatedly on the same
sample, such as in single-cell biology. Optimal transport (OT) can solve this
challenge by learning an optimal coupling of samples across distributions from
unpaired data. However, the usual formulation of OT assumes conservation of
mass, which is violated in unbalanced scenarios in which the population size
changes (e.g., cell proliferation or death) between measurements. In this work,
we introduce NubOT, a neural unbalanced OT formulation that relies on the
formalism of semi-couplings to account for creation and destruction of mass. To
estimate such semi-couplings and generalize out-of-sample, we derive an
efficient parameterization based on neural optimal transport maps and propose a
novel algorithmic scheme through a cycle-consistent training procedure. We
apply our method to the challenging task of forecasting heterogeneous responses
of multiple cancer cell lines to various drugs, where we observe that by
accurately modeling cell proliferation and death, our method yields notable
improvements over previous neural optimal transport methods
Characterization of the neurogenic niche in the aging dentate gyrus using iterative immunofluorescence imaging
Advancing age causes reduced hippocampal neurogenesis, associated with age-related cognitive decline. The spatial relationship of age-induced alterations in neural stem cells (NSCs) and surrounding cells within the hippocampal niche remains poorly understood due to limitations of antibody-based cellular phenotyping. We established iterative indirect immunofluorescence imaging (4i) in tissue sections, allowing for simultaneous detection of 18 proteins to characterize NSCs and surrounding cells in 2-, 6-, and 12-month-old mice. We show that reorganization of the dentate gyrus (DG) niche already occurs in middle-aged mice, paralleling the decline in neurogenesis. 4i-based tissue analysis of the DG identifies changes in cell-type contributions to the blood-brain barrier and microenvironments surrounding NSCs to play a pivotal role to preserve neurogenic permissiveness. The data provided represent a resource to characterize the principles causing alterations of stem cell-associated plasticity within the aging DG and provide a blueprint to analyze somatic stem cell niches across lifespan in complex tissues
Multimodal perception links cellular state to decision-making in single cells
Individual cells make decisions that are adapted to their internal state and surroundings, but how cells can reliably do this remains unclear. To study the information processing capacity of human cells, we conducted multiplexed quantification of signaling responses and markers of the cellular state. Signaling nodes in a network displayed adaptive information processing, which led to heterogeneous growth factor responses and enabled nodes to capture partially nonredundant information about the cellular state. Collectively, as a multimodal percept this gives individual cells a large information processing capacity to accurately place growth factor concentration within the context of their cellular state and make cellular state–dependent decisions. Heterogeneity and complexity in signaling networks may have coevolved to enable specific and context-aware cellular decision-making in a multicellular setting
Learning Single-Cell Perturbation Responses using Neural Optimal Transport
The ability to understand and predict molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CellOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CellOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.Competing Interest StatementG.G. and L.P. have filed a patent on the 4i technology (patentWO2019207004A1)
Learning single-cell perturbation responses using neural optimal transport
Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-beta and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.ISSN:1548-7105ISSN:1548-709
Multimodal spatiotemporal phenotyping of human retinal organoid development
Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial scales and molecular modalities are lacking. In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue. We developed a toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components and global patterning in each organoid and primary tissue. In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation.ISSN:1546-1696ISSN:1087-015
The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support
The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease