Mapping the Pairwise Choices Leading from Pluripotency to Human Bone, Heart, and Other Mesoderm Cell Types.

Stem-cell differentiation to desired lineages requires navigating alternating developmental paths that often lead to unwanted cell types. Hence, comprehensive developmental roadmaps are crucial to channel stem-cell differentiation toward desired fates. To this end, here, we map bifurcating lineage choices leading from pluripotency to 12 human mesodermal lineages, including bone, muscle, and heart. We defined the extrinsic signals controlling each binary lineage decision, enabling us to logically block differentiation toward unwanted fates and rapidly steer pluripotent stem cells toward 80%-99% pure human mesodermal lineages at most branchpoints. This strategy enabled the generation of human bone and heart progenitors that could engraft in respective in vivo models. Mapping stepwise chromatin and single-cell gene expression changes in mesoderm development uncovered somite segmentation, a previously unobservable human embryonic event transiently marked by HOPX expression. Collectively, this roadmap enables navigation of mesodermal development to produce transplantable human tissue progenitors and uncover developmental processes. VIDEO ABSTRACT.

Mobility network models of COVID-19 explain inequities and inform reopening.

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.

Impossibility theorems for feature attribution.

Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way. In this paper, we show that for moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear-for example, Integrated Gradients and Shapley Additive Explanations (SHAP)-can provably fail to improve on random guessing for inferring model behavior. Our results apply to common end-tasks such as characterizing local model behavior, identifying spurious features, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks: Once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods.

Localized hepatic lobular regeneration by central-vein-associated lineage-restricted progenitors.

The regeneration of organ morphology and function following tissue loss is critical to restore normal physiology, yet few cases are documented in mammalian postnatal life. Partial hepatectomy of the adult mammalian liver activates compensatory hepatocyte hypertrophy and cell division across remaining lobes, resulting in restitution of organ mass but with permanent alteration of architecture. Here, we identify a time window in early postnatal life wherein partial amputation culminates in a localized regeneration instead of global hypertrophy and proliferation. Quantifications of liver mass, enzymatic activity, and immunohistochemistry demonstrate that damaged lobes underwent multilineage regeneration, reforming a lobe often indistinguishable from undamaged ones. Clonal analysis during regeneration reveals local clonal expansions of hepatocyte stem/progenitors at injured sites that are lineage but not fate restricted. Tetrachimeric mice show clonal selection occurs during development with further selections following injury. Surviving progenitors associate mainly with central veins, in a pattern of selection different from that of normal development. These results illuminate a previously unknown program of liver regeneration after acute injury and allow for exploration of latent regenerative programs with potential applications to adult liver regeneration.

Denoising genome-wide histone ChIP-seq with convolutional neural networks.

MOTIVATION: Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by many experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging. RESULTS: We introduce a convolutional denoising algorithm, Coda, that uses convolutional neural networks to learn a mapping from suboptimal to high-quality histone ChIP-seq data. This overcomes various sources of noise and variability, substantially enhancing and recovering signal when applied to low-quality chromatin profiling datasets across individuals, cell types and species. Our method has the potential to improve data quality at reduced costs. More broadly, this approach-using a high-dimensional discriminative model to encode a generative noise process-is generally applicable to other biological domains where it is easy to generate noisy data but difficult to analytically characterize the noise or underlying data distribution. AVAILABILITY AND IMPLEMENTATION: https://github.com/kundajelab/coda . CONTACT: akundaje@stanford.edu.

Inferring Multidimensional Rates of Aging from Cross-Sectional Data.

Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.

An atlas of transcriptional, chromatin accessibility, and surface marker changes in human mesoderm development.

Mesoderm is the developmental precursor to myriad human tissues including bone, heart, and skeletal muscle. Unravelling the molecular events through which these lineages become diversified from one another is integral to developmental biology and understanding changes in cellular fate. To this end, we developed an in vitro system to differentiate human pluripotent stem cells through primitive streak intermediates into paraxial mesoderm and its derivatives (somites, sclerotome, dermomyotome) and separately, into lateral mesoderm and its derivatives (cardiac mesoderm). Whole-population and single-cell analyses of these purified populations of human mesoderm lineages through RNA-seq, ATAC-seq, and high-throughput surface marker screens illustrated how transcriptional changes co-occur with changes in open chromatin and surface marker landscapes throughout human mesoderm development. This molecular atlas will facilitate study of human mesoderm development (which cannot be interrogated in vivo due to restrictions on human embryo studies) and provides a broad resource for the study of gene regulation in development at the single-cell level, knowledge that might one day be exploited for regenerative medicine.

Dissecting an online intervention for cancer survivors: four exploratory analyses of internet engagement and its effects on health status and health behaviors.

The Internet has been used extensively to offer health education content and also for social support. More recently, we have seen the advent of Internet-based health education interventions that combine content with structured social networking. In many ways this is the Internet equivalent to small group interventions. While we have some knowledge about the efficacy of these interventions, few studies have examined how participants engage with programs and how that might affect outcomes. This study seeks to explore (a) the content of posts and (b) the nature of participant engagement with an online, 6-week workshop for cancer survivors and how such engagement may affect health outcomes. Using methodologies related to computational linguistics (latent Dirichlet allocation) and more standard statistical approaches, we identified (a) discussion board themes; (b) the relationship between reading and posting messages and outcomes; (c) how making, completing, or not completing action plans is related to outcome; and (d) how self-tailoring relates to outcomes. When considering all posts, emotional support is a key theme. However, different sets of themes are expressed in the first workshop post where participants are asked to express their primary concern. Writing posts was related to improved outcomes, but reading posts was less important. Completing, but not merely making, action plans and self-tailoring are statistically associated with future positive health outcomes. The findings from these exploratory studies can be considered when shaping future electronically mediated social networking interventions. In addition, the methods used here can be used in analyzing other large electronically mediated social-networking interventions.